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Will I be in need of my old computer? | 0 | I have a 3080 PC that I am replacing with 5090, and will be looking to delve into dual boot set up windows for gaming and linux in the new machine so I can get into the world of local LLMs. I have a very long way to catch up as I haven’t coded in 20 years.
My question is if there is an obvious use case for having two computers in a journey to discover deeper AI, local LLMs and/or immage diffusion models, and other peripheral services like maybe use it as data server or online connection testing etc? otherwise I might sell and/or gift the old computer away.
| 2025-11-05T11:03:38 | https://www.reddit.com/r/LocalLLaMA/comments/1oozwve/will_i_be_in_need_of_my_old_computer/ | bartem33 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oozwve | false | null | t3_1oozwve | /r/LocalLLaMA/comments/1oozwve/will_i_be_in_need_of_my_old_computer/ | false | false | self | 0 | null |
Need help finetuning 😭 | 0 | Am a fresh uni student and my project was to fine tune gemma3 4b on Singapore's constitution
I made a script to chunk then embed into faiss indexes then call each chunk to generate a question answer pair with gemma3 4b running on ollama
The outputs are accurate but short
For finetuning i used MLX on a base M4 mini
The loss seems fine ending at 1.8 after 4000iter and batchsize of 3 at 12layers deep
But when i use the model its trash not only it dosent know about constitution even normal questioning its fumbling
How do i fix it i have a week to submit this assignment 😭 | 2025-11-05T10:56:50 | https://www.reddit.com/r/LocalLLaMA/comments/1oozskz/need_help_finetuning/ | Immediate_Lock7595 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oozskz | false | null | t3_1oozskz | /r/LocalLLaMA/comments/1oozskz/need_help_finetuning/ | false | false | self | 0 | null |
aquif-3.5-Max-42B-A3B | 88 | Beats GLM 4.6 according to provided benchmarks
Million context
Apache 2.0
Works both with GGUF/llama.cpp and MLX/lmstudio out-of-box, as it's qwen3_moe architecture | 2025-11-05T10:27:09 | https://huggingface.co/aquif-ai/aquif-3.5-Max-42B-A3B | CoruNethronX | huggingface.co | 1970-01-01T00:00:00 | 0 | {} | 1oozb8v | false | null | t3_1oozb8v | /r/LocalLLaMA/comments/1oozb8v/aquif35max42ba3b/ | false | false | 88 | {'enabled': False, 'images': [{'id': 'iQbBlhyprDj6yO7lC6_VBVFWSoqEqFZE8HYk4JH2vHI', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/iQbBlhyprDj6yO7lC6_VBVFWSoqEqFZE8HYk4JH2vHI.png?width=108&crop=smart&auto=webp&s=ad5b7ef36312e63cf69e05340181f0b856bf197b', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/iQbBlhyprDj6yO7lC6_VBVFWSoqEqFZE8HYk4JH2vHI.png?width=216&crop=smart&auto=webp&s=81b4ef3555871c54a17753ead7f041a461a160b2', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/iQbBlhyprDj6yO7lC6_VBVFWSoqEqFZE8HYk4JH2vHI.png?width=320&crop=smart&auto=webp&s=623796659f44f106f132192ad4168b88242894a9', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/iQbBlhyprDj6yO7lC6_VBVFWSoqEqFZE8HYk4JH2vHI.png?width=640&crop=smart&auto=webp&s=bf206a8e60f2f9eb24b0998337979fd6ea8d653c', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/iQbBlhyprDj6yO7lC6_VBVFWSoqEqFZE8HYk4JH2vHI.png?width=960&crop=smart&auto=webp&s=ff86794ff0e2fb0a85b7508918b9a6ff16aa72aa', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/iQbBlhyprDj6yO7lC6_VBVFWSoqEqFZE8HYk4JH2vHI.png?width=1080&crop=smart&auto=webp&s=0b5143b895e33bfeaf2697748b0bba4e5a39a559', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/iQbBlhyprDj6yO7lC6_VBVFWSoqEqFZE8HYk4JH2vHI.png?auto=webp&s=2551d7d9e0f8c7ab4b304da1d3c7c37735bda960', 'width': 1200}, 'variants': {}}]} | |
Trajectory Distillation for Foundation Models | 0 | In most labs, the cost of **post-training** the foundation models sits at the edge of feasibility. I mean we are in the scaling era. And RL remains powerful, but sparse rewards make it inefficient, expensive, and hard to stabilize. This is clearly mentioned in the Thinking Machines latest post "On-Policy Distillation." It presents a leaner alternative—trajectory distillation—that preserves reasoning depth while cutting compute by an order of magnitude.
Here’s the core mechanism:
>
The results that are presented in the blog:
* Qwen3-8B reached 74.4 % on AIME’24; matching RL pipelines at roughly \*10× lower cost.
* Learning remains stable even when the student diverges from the teacher’s prior trajectory.
* Instruction-following and reasoning fidelity are fully recoverable after domain-specific mid-training.
What makes this compelling to me is its shift in emphasis. Instead of compressing parameters, trajectory distillation compresses the reasoning structure.
So, could dense supervision ultimately replace RL as the dominant post-training strategy for foundation models?
And if so, what new forms of “reasoning evaluation” will we need to prove alignment across scales?
Curious to hear perspectives—especially from anyone experimenting with on-policy distillation or process-reward modeling.
Citations:
1. [On-Policy Distillation](https://thinkingmachines.ai/blog/on-policy-distillation/)
2. [A Theoretical Understanding of Foundation Models](https://go.adaline.ai/NoX0UZz) | 2025-11-05T09:56:18 | https://www.reddit.com/r/LocalLLaMA/comments/1ooytlg/trajectory_distillation_for_foundation_models/ | TheProdigalSon26 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1ooytlg | false | null | t3_1ooytlg | /r/LocalLLaMA/comments/1ooytlg/trajectory_distillation_for_foundation_models/ | false | false | self | 0 | {'enabled': False, 'images': [{'id': '64I_Vj7UCXHVlrN4JFEla6FoVK7pK-s2YD6oc5guXzI', 'resolutions': [{'height': 51, 'url': 'https://external-preview.redd.it/64I_Vj7UCXHVlrN4JFEla6FoVK7pK-s2YD6oc5guXzI.png?width=108&crop=smart&auto=webp&s=eefd51e9f56307c4748cfd91cb6733eaf6933bdd', 'width': 108}, {'height': 102, 'url': 'https://external-preview.redd.it/64I_Vj7UCXHVlrN4JFEla6FoVK7pK-s2YD6oc5guXzI.png?width=216&crop=smart&auto=webp&s=45ee571d4b75e8d8ea1ff1a678bc467ccb155849', 'width': 216}, {'height': 152, 'url': 'https://external-preview.redd.it/64I_Vj7UCXHVlrN4JFEla6FoVK7pK-s2YD6oc5guXzI.png?width=320&crop=smart&auto=webp&s=c383fc180a66d9fc5b2b26a59e4311b1dff825f7', 'width': 320}, {'height': 304, 'url': 'https://external-preview.redd.it/64I_Vj7UCXHVlrN4JFEla6FoVK7pK-s2YD6oc5guXzI.png?width=640&crop=smart&auto=webp&s=ba2c34d00d8a36414556653c004de1a08d84e4a8', 'width': 640}, {'height': 456, 'url': 'https://external-preview.redd.it/64I_Vj7UCXHVlrN4JFEla6FoVK7pK-s2YD6oc5guXzI.png?width=960&crop=smart&auto=webp&s=da374247ba73d555c17a5b437b885b9ea4502c17', 'width': 960}, {'height': 513, 'url': 'https://external-preview.redd.it/64I_Vj7UCXHVlrN4JFEla6FoVK7pK-s2YD6oc5guXzI.png?width=1080&crop=smart&auto=webp&s=7bf55d6a67d995ed450e12d32d34b918450d9333', 'width': 1080}], 'source': {'height': 1140, 'url': 'https://external-preview.redd.it/64I_Vj7UCXHVlrN4JFEla6FoVK7pK-s2YD6oc5guXzI.png?auto=webp&s=2b5a42e27027af9d5a6dbb838bb8854f40bb631c', 'width': 2400}, 'variants': {}}]} |
llama.cpp and llama-server VULKAN using CPU | 4 | as the title says , llama.cpp and llama-server VULKAN appears to be using CPU. I only noticed when i went back to LM Studio and got double the speed and my Computer didnt sound like it was about to take off.
everything looks good, but just doesnt make sense :
load\_backend: loaded RPC backend from C:\\llama\\ggml-rpc.dll
ggml\_vulkan: Found 1 Vulkan devices:
ggml\_vulkan: 0 = AMD Radeon RX 6700 XT (AMD proprietary driver) | uma: 0 | fp16: 1 | bf16: 0 | warp size: 32 | shared memory: 32768 | int dot: 1 | matrix cores: none
load\_backend: loaded Vulkan backend from C:\\llama\\ggml-vulkan.dll
load\_backend: loaded CPU backend from C:\\llama\\ggml-cpu-haswell.dll
build: 6923 (76af40aaa) with clang version 19.1.5 for x86\_64-pc-windows-msvc
system info: n\_threads = 6, n\_threads\_batch = 6, total\_threads = 12
system\_info: n\_threads = 6 (n\_threads\_batch = 6) / 12 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | BMI2 = 1 | LLAMAFILE = 1 | OPENMP = 1 | REPACK = 1 |
| 2025-11-05T09:51:51 | https://www.reddit.com/r/LocalLLaMA/comments/1ooyr6r/llamacpp_and_llamaserver_vulkan_using_cpu/ | uber-linny | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1ooyr6r | false | null | t3_1ooyr6r | /r/LocalLLaMA/comments/1ooyr6r/llamacpp_and_llamaserver_vulkan_using_cpu/ | false | false | self | 4 | null |
Is vLLM about to hit the wall? | 0 | Remember when vLLM was the undisputed champ for blazing-fast inference? Yeah, those days might be numbered. I'm starting to think its time at the top is drawing to a close, and a serious contender is going to show up and basically push it out of the spotlight.
Why the doom-and-gloom prediction? It all boils down to the trainwreck of a split between its academic founders and its corporate backer.
The academic folks at least seem to be playing it straight, keeping things open and genuine. But the sponsor side? They're clearly drinking their own Kool-Aid and seem more interested in plugging their own low-tech ventures and generating hype (just check out the noise they made at the recent PyTorch conferences).
It’s a total bait-and-switch with the community. They *act* like they want independent contributions on open forums, but if you're not coming in with a big corporate logo stamped on your forehead, you're quietly frozen out.
And here’s the real kicker: they put on a show of open support on github, but behind the scenes, it looks like technical debt is piling up fast. Design flaws are sneaking in, the kind of insidious bugs that are a nightmare to track down. And to top it off, they seem to be actively ignoring solid fixes from serious outside contributors. This lack of authenticity, especially from the corporate half, is creating massive design debt and Becoming increasingly brittle and fragile.
Frankly, the business side seems completely sidetracked, only caring about other major sponsors and their clients. Meanwhile, they're over-hyping vllm itself to oblivion.
My read? vllm has lost the very thing that made it great: the engine of genuine, grassroots community effort. It’s not a question of *if* but when a new, more honest project steps up to take its crown. It's just a matter of time before someone builds a better mousetrap. | 2025-11-05T09:50:00 | https://www.reddit.com/r/LocalLLaMA/comments/1ooyq59/is_vllm_about_to_hit_the_wall/ | Pitiful-Reindeer6980 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1ooyq59 | false | null | t3_1ooyq59 | /r/LocalLLaMA/comments/1ooyq59/is_vllm_about_to_hit_the_wall/ | false | false | self | 0 | null |
Hephaestus: AI workflows that discover and create their own tasks as they work | 20 | Hey everyone! 👋
A week ago I shared Hephaestus - an open-source framework where AI agents dynamically build workflows based on what they discover. The
response has been incredible (500+ stars already!)
**The Core Idea:** Instead of predefining every task upfront, you define *phase types* (like "Analyze → Implement → Test"), and agents
create specific tasks across these phases based on what they discover as they work.
**Real Example:** Give it a PRD for "Build a REST API with authentication." A Phase 1 agent analyzes it and spawns 5 implementation tasks
(auth system, database, API layer, tests, deployment). A Phase 3 validation agent testing the auth system discovers an elegant caching
pattern that could speed up all API routes by 40%. Instead of being stuck or following rigid branching logic, it spawns a Phase 1
investigation task. Another agent picks it up, confirms it's viable, spawns a Phase 2 implementation task. The workflow just branched
itself based on discovery.
**What makes it different:**
- 🔄 **Self-building workflows** - Agents spawn tasks dynamically, not predefined branches
- 🧠 **RAG-powered coordination** - Agents share discoveries through semantic memory
- 🎯 **Guardian monitoring** - Continuously tracks agent trajectories to prevent drift
- 📊 **Kanban coordination** - Real-time task management with blocking relationships
- And so much more...
🔗 **GitHub:** https://github.com/Ido-Levi/Hephaestus
📚 **Docs:** https://ido-levi.github.io/Hephaestus/
Fair warning: This is still new and rough around the edges. Issues and feedback are very welcome, and I'm happy to review contributions! | 2025-11-05T09:43:16 | https://v.redd.it/fdwlhddkrezf1 | Standard_Excuse7988 | v.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1ooymcm | false | {'reddit_video': {'bitrate_kbps': 2400, 'dash_url': 'https://v.redd.it/fdwlhddkrezf1/DASHPlaylist.mpd?a=1764927812%2CMDcxYzIxMDA5MjVhNzA0MzhkODY1ZTdmNzVmNTUwMjUyYThkNTk1ZmE1MjcxMjIxODAzNGMzNGFkMDFiNGRhMA%3D%3D&v=1&f=sd', 'duration': 89, 'fallback_url': 'https://v.redd.it/fdwlhddkrezf1/DASH_720.mp4?source=fallback', 'has_audio': False, 'height': 554, 'hls_url': 'https://v.redd.it/fdwlhddkrezf1/HLSPlaylist.m3u8?a=1764927812%2COTAwMWNlMjIwY2YzYzQxMDg2ZTIxZGEyZTczNjg4MWYwMGQxOWM0Yjc0YTE0NzRkM2EzMWE4Y2I5MWIxNDQxNQ%3D%3D&v=1&f=sd', 'is_gif': False, 'scrubber_media_url': 'https://v.redd.it/fdwlhddkrezf1/DASH_96.mp4', 'transcoding_status': 'completed', 'width': 1280}} | t3_1ooymcm | /r/LocalLLaMA/comments/1ooymcm/hephaestus_ai_workflows_that_discover_and_create/ | false | false | 20 | {'enabled': False, 'images': [{'id': 'Y3kwMnpjZGtyZXpmMdAkQHndWWJHnvJDGx1uxfxLVUHIG9vtIHStiltBck3-', 'resolutions': [{'height': 46, 'url': 'https://external-preview.redd.it/Y3kwMnpjZGtyZXpmMdAkQHndWWJHnvJDGx1uxfxLVUHIG9vtIHStiltBck3-.png?width=108&crop=smart&format=pjpg&auto=webp&s=f205d641e80fb96eed9787cfc404eb57f82af1e9', 'width': 108}, {'height': 93, 'url': 'https://external-preview.redd.it/Y3kwMnpjZGtyZXpmMdAkQHndWWJHnvJDGx1uxfxLVUHIG9vtIHStiltBck3-.png?width=216&crop=smart&format=pjpg&auto=webp&s=c7cd4623ebdcc23c20837a20dab68534275e5a91', 'width': 216}, {'height': 138, 'url': 'https://external-preview.redd.it/Y3kwMnpjZGtyZXpmMdAkQHndWWJHnvJDGx1uxfxLVUHIG9vtIHStiltBck3-.png?width=320&crop=smart&format=pjpg&auto=webp&s=0dc2d4ac65e3e954e6198ffe4dbdc48977ed29f7', 'width': 320}, {'height': 276, 'url': 'https://external-preview.redd.it/Y3kwMnpjZGtyZXpmMdAkQHndWWJHnvJDGx1uxfxLVUHIG9vtIHStiltBck3-.png?width=640&crop=smart&format=pjpg&auto=webp&s=78b7f227d5035b0d9e9271fc254e27049dc6cdfc', 'width': 640}, {'height': 415, 'url': 'https://external-preview.redd.it/Y3kwMnpjZGtyZXpmMdAkQHndWWJHnvJDGx1uxfxLVUHIG9vtIHStiltBck3-.png?width=960&crop=smart&format=pjpg&auto=webp&s=0116f8b4dacc9dc637ad9020e2ce82ed3b313f4a', 'width': 960}, {'height': 466, 'url': 'https://external-preview.redd.it/Y3kwMnpjZGtyZXpmMdAkQHndWWJHnvJDGx1uxfxLVUHIG9vtIHStiltBck3-.png?width=1080&crop=smart&format=pjpg&auto=webp&s=88dd7470ac1af7991ce252ee7226456e8f596360', 'width': 1080}], 'source': {'height': 754, 'url': 'https://external-preview.redd.it/Y3kwMnpjZGtyZXpmMdAkQHndWWJHnvJDGx1uxfxLVUHIG9vtIHStiltBck3-.png?format=pjpg&auto=webp&s=1dabbc9bccb30eb6967f944c39f1818247f4f57c', 'width': 1744}, 'variants': {}}]} | |
Ideal LocalLLM setup for Windows with RTX 3080? | 1 | Hi, I’m using a Windows PC with an AMD 3900x CPU, 64GB RAM, and an RTX 3080 (10GB). I need to process around 100k requests in total, with each request processing about 110k tokens.
I’m quite satisfied with the output quality from **Qwen3:8B\_K\_M** on Ollama, but the performance is a major issue — each request takes around 10 minutes to complete.
When I check Task Manager, the CPU usage is about 70%, but the GPU utilization fluctuates randomly between 1–30%, which seems incorrect.
I am also have Mac M4 16G RAM/256G SSD.
What could be causing this, and what’s the best way to optimize for this workload? | 2025-11-05T09:42:17 | https://www.reddit.com/r/LocalLLaMA/comments/1ooyls3/ideal_localllm_setup_for_windows_with_rtx_3080/ | -ScaTteRed- | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1ooyls3 | false | null | t3_1ooyls3 | /r/LocalLLaMA/comments/1ooyls3/ideal_localllm_setup_for_windows_with_rtx_3080/ | false | false | self | 1 | null |
LMArena.ai Paradox: Votes Flow 24/7, But the Leaderboard is Frozen for Weeks. What's the Point? | 4 | Hey, r/LocalLLaMA!
I have a REALLY HUGE question for you guys. It's about [LMArena.ai](http://LMArena.ai) and their absolutely weird ranking updates. I'm a regular there, and this whole setup just keeps breaking my brain, to be honest.
We keep voting in these "Battles" every single day, bringing them tons of super-fresh data on which LLMs people are into. But the leaderboard? BUT WHAT THE HELL!? It can just be frozen for weeks. That seriously pisses me off, and makes you wonder: can we even trust this site at all?
\-----------
The Main Question: Why are We Wasting Time?
If my votes today aren't going to budge the rating for like, two weeks, what's the point of even showing up?! It honestly feels like the site is turning into some kind of shady data vacuum with zero real payback.
And seriously: if the admins are filtering those votes anyway, why not just put out an official statement about a schedule? Like, "updates strictly every Monday" or something? The lack of transparency is the biggest killer here.
\----------
The Elo Paradox
Logically, shouldn't those Elo scores be changing incrementally, little by little, as votes come in? But NO! They just dump a giant load of data at once, and BOOM! -ratings jump all over the place for absolutely no reason. This totally disconnects the rank from how the models are actually performing day-to-day. So we're just stuck staring at "yesterday's news" and we have no clue which model is actually crushing it right now.
\----------
The "Hype" Favoritism
This is the most annoying part.
When some super-hyped, new model drops (looking at you, Google or Anthropic), they throw it onto the board instantly. But what about smaller, Open-Source models????????? They can be left off for weeks, sometimes even longer. Seriously, it looks like they're just chasing commercial hype, instead of running a fair and consistent benchmark for everyone.
\----------
So, what do you guys think? | 2025-11-05T09:31:42 | https://www.reddit.com/r/LocalLLaMA/comments/1ooyg33/lmarenaai_paradox_votes_flow_247_but_the/ | ThetaCursed | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1ooyg33 | false | null | t3_1ooyg33 | /r/LocalLLaMA/comments/1ooyg33/lmarenaai_paradox_votes_flow_247_but_the/ | false | false | self | 4 | {'enabled': False, 'images': [{'id': 'dxaa3VkSFfl76ZnRXbLjhaUzWdfNAAxeWXabxezBV5c', 'resolutions': [{'height': 56, 'url': 'https://external-preview.redd.it/dxaa3VkSFfl76ZnRXbLjhaUzWdfNAAxeWXabxezBV5c.jpeg?width=108&crop=smart&auto=webp&s=f2daeb57c3abc17ba4b82884e50ef61b0a11706c', 'width': 108}, {'height': 113, 'url': 'https://external-preview.redd.it/dxaa3VkSFfl76ZnRXbLjhaUzWdfNAAxeWXabxezBV5c.jpeg?width=216&crop=smart&auto=webp&s=c863cfe705ea8d58b2d717500785b7f6201f4b1c', 'width': 216}, {'height': 168, 'url': 'https://external-preview.redd.it/dxaa3VkSFfl76ZnRXbLjhaUzWdfNAAxeWXabxezBV5c.jpeg?width=320&crop=smart&auto=webp&s=9f5a3c3b38f688a14ac1ef7f52efddd44886f7fa', 'width': 320}, {'height': 336, 'url': 'https://external-preview.redd.it/dxaa3VkSFfl76ZnRXbLjhaUzWdfNAAxeWXabxezBV5c.jpeg?width=640&crop=smart&auto=webp&s=6d10fd8af9228e9286dbf92733b3f4f1e7b99e6c', 'width': 640}, {'height': 504, 'url': 'https://external-preview.redd.it/dxaa3VkSFfl76ZnRXbLjhaUzWdfNAAxeWXabxezBV5c.jpeg?width=960&crop=smart&auto=webp&s=d0adae25e578a35ca295008baa28f6b9bb28cd1d', 'width': 960}, {'height': 567, 'url': 'https://external-preview.redd.it/dxaa3VkSFfl76ZnRXbLjhaUzWdfNAAxeWXabxezBV5c.jpeg?width=1080&crop=smart&auto=webp&s=7787c0bc033b971e2c52e09af1d9bf34926a8765', 'width': 1080}], 'source': {'height': 630, 'url': 'https://external-preview.redd.it/dxaa3VkSFfl76ZnRXbLjhaUzWdfNAAxeWXabxezBV5c.jpeg?auto=webp&s=fecf29dcf1824728952d779499b746c807f6aa64', 'width': 1200}, 'variants': {}}]} |
What local model for MCP? | 1 | Hello,
I’m building an open source alternative to Poke.com that runs on your own hardware. I have a few MCPs that returns confidential information (location history, banking details, emails) that are used to augment responses and make it more useful and I’d like to only expose those tools to a local model.
I’m not that much knowledgeable about local models though, is there any that supports MCP well enough and can do some very basic data transformation? Ideally fitting in a 8Gb GPU as it seems to be what most (common) people have for AI at home. | 2025-11-05T09:29:14 | https://www.reddit.com/r/LocalLLaMA/comments/1ooyers/what_local_model_for_mcp/ | Affectionate-Dress-4 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1ooyers | false | null | t3_1ooyers | /r/LocalLLaMA/comments/1ooyers/what_local_model_for_mcp/ | false | false | self | 1 | null |
Looking for the best framework for a multi-agentic AI system — beyond LangGraph, Toolformer, LlamaIndex, and Parlant | 4 | I’m starting work on a multi-agentic AI system and I’m trying to decide which framework would be the most solid choice.
I’ve been looking into LangGraph, Toolformer, LlamaIndex, and Parlant, but I’m not sure which ecosystem is evolving fastest or most suitable for complex agent coordination.
Do you know of any other frameworks or libraries focused on multi-agent reasoning, planning, and tool use that are worth exploring right now? | 2025-11-05T08:59:21 | https://www.reddit.com/r/LocalLLaMA/comments/1ooxybf/looking_for_the_best_framework_for_a_multiagentic/ | Spinotesla | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1ooxybf | false | null | t3_1ooxybf | /r/LocalLLaMA/comments/1ooxybf/looking_for_the_best_framework_for_a_multiagentic/ | false | false | self | 4 | null |
GLM 4.6 AIR is coming....? | 246 | or not yet? What do you think? | 2025-11-05T08:43:40 | jacek2023 | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1ooxple | false | null | t3_1ooxple | /r/LocalLLaMA/comments/1ooxple/glm_46_air_is_coming/ | false | false | 246 | {'enabled': True, 'images': [{'id': 'hcfRvMM3QALvdFrEpKuIUJRZO5kI9Qfs_cN8Wt4Savc', 'resolutions': [{'height': 32, 'url': 'https://preview.redd.it/56uu0u1fiezf1.png?width=108&crop=smart&auto=webp&s=5e4e96798300b4aa71ec0921dabd4ad1a73501b7', 'width': 108}, {'height': 64, 'url': 'https://preview.redd.it/56uu0u1fiezf1.png?width=216&crop=smart&auto=webp&s=8099d24d59ee976a931bbd47d997849f80fbe2a8', 'width': 216}, {'height': 96, 'url': 'https://preview.redd.it/56uu0u1fiezf1.png?width=320&crop=smart&auto=webp&s=4748db1df48b7ce94e935ab40a291000e001166f', 'width': 320}], 'source': {'height': 153, 'url': 'https://preview.redd.it/56uu0u1fiezf1.png?auto=webp&s=4d9454eabde7a9fc563fc8633070da758e1adbb7', 'width': 510}, 'variants': {}}]} | ||
Have we figured out any good solutions around the MoE finetuning issues? (other than GSPO) | 5 | Was wondering if we had a more elegant solution yet for offline-policy training methods (like dpo and it's variants) than just not training on the router layer. Last I checked only GSPO training worked well, but that's pretty expensive. | 2025-11-05T08:10:35 | https://www.reddit.com/r/LocalLLaMA/comments/1oox7pq/have_we_figured_out_any_good_solutions_around_the/ | lemon07r | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oox7pq | false | null | t3_1oox7pq | /r/LocalLLaMA/comments/1oox7pq/have_we_figured_out_any_good_solutions_around_the/ | false | false | self | 5 | null |
Un-LOCC Wrapper: I built a Python library that compresses your OpenAI chats into images, saving up to 3× on tokens! (or even more :D) | 16 | **TL;DR**: I turned my optical compression research into an **actual Python library** that wraps the OpenAI SDK. Now you can compress large text contexts into images with a simple `compressed: True` flag, achieving **up to 2.8:1 token compression** while maintaining over **93% accuracy**. Drop-in replacement for OpenAI client - sync/async support included.
**GitHub:** [https://github.com/MaxDevv/Un-LOCC-Wrapper](https://github.com/MaxDevv/Un-LOCC-Wrapper)
# What this is:
**Un-LOCC Wrapper** \- A Python library that takes my optical compression research and makes it **actually usable** in your projects today. It's a simple wrapper around the OpenAI SDK that automatically converts text to compressed images when you add a `compressed: True` flag.
# How it works:
* Render text into optimized images (using research-tested fonts/sizes)
* Pass images to Vision-Language Models instead of text tokens
* Get the same responses while using WAY fewer tokens
# Code Example - It's this simple:
from un_locc import UnLOCC
client = UnLOCC(api_key="your-api-key")
# Compress large context with one flag
messages = [
{"role": "user", "content": "Summarize this document:"},
{"role": "user", "content": large_text, "compressed": True} # ← That's it!
]
response = client.chat.completions.create(
model="gpt-4o",
messages=messages
)
**Async version too:**
from un_locc import AsyncUnLOCC
client = AsyncUnLOCC(api_key="your-api-key")
response = await client.chat.completions.create(...)
# Key Features:
* 🚀 **Drop-in replacement** for OpenAI client
* ⚡ **Sync & async** support
* 🎯 **Research-backed defaults** (Atkinson Hyperlegible font, 864×864px, etc.)
* 🔧 **Customizable** \- override any compression parameter
* 📚 **Works with** chat completions & responses API
* 🏎️ **Fast rendering** \- ReportLab + pypdfium2 when available
# Why this matters:
* **Pay \~3× less** for context tokens
* **Extend context windows** without expensive upgrades
* **Perfect for**: chat history compression, document analysis, large-context workflows
* **Zero model changes** \- works with existing VLMs like GPT-4o
# The Research Behind It:
Based on my [UN-LOCC research](https://github.com/MaxDevv/UN-LOCC) testing 90+ experiments across 6+ VLMs:
* **Gemini 2.0 Flash Lite**: 93.65% accuracy @ 2.8:1 compression
* **Qwen2.5-VL-72B**: 99.26% accuracy @ 1.7:1 compression
* **Qwen3-VL-235B**: 95.24% accuracy @ 2.2:1 compression
# Install & Try:
pip install un-locc
The library handles all the complexity - fonts, rendering optimization, content type detection. You just add `compressed: True` and watch your token usage plummet.
**GitHub repo (stars help a ton!):** [https://github.com/MaxDevv/Un-LOCC-Wrapper](https://github.com/MaxDevv/Un-LOCC-Wrapper)
**Quick Note**: While testing the library beyond my original research, I discovered that the compression limits are actually MUCH higher than the conservative 3x I reported. Gemini was consistently understanding text and accurately reading back sentences at **6x compression** without issues. The 3x figure was just my research cutoff for quantifiable accuracy metrics, but for real-world use cases where perfect character-level retrieval isn't critical, we're looking at, maybe something like... **6-7x compression** lol :D | 2025-11-05T07:46:54 | https://www.reddit.com/r/LocalLLaMA/comments/1oowuqd/unlocc_wrapper_i_built_a_python_library_that/ | MaxDev0 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oowuqd | false | null | t3_1oowuqd | /r/LocalLLaMA/comments/1oowuqd/unlocc_wrapper_i_built_a_python_library_that/ | false | false | self | 16 | {'enabled': False, 'images': [{'id': 'nQ-yLReX_JB0QMh9hKI8nB2d3e55NeE-IgzYnxCigI4', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/nQ-yLReX_JB0QMh9hKI8nB2d3e55NeE-IgzYnxCigI4.png?width=108&crop=smart&auto=webp&s=ddb01fca5bb606aa224c57abbcd8d5f42e6ac233', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/nQ-yLReX_JB0QMh9hKI8nB2d3e55NeE-IgzYnxCigI4.png?width=216&crop=smart&auto=webp&s=42ddb741980ee1de1ce7b3edcff9080aca694c02', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/nQ-yLReX_JB0QMh9hKI8nB2d3e55NeE-IgzYnxCigI4.png?width=320&crop=smart&auto=webp&s=a25d6794599572e884caca9465502a506d1fc338', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/nQ-yLReX_JB0QMh9hKI8nB2d3e55NeE-IgzYnxCigI4.png?width=640&crop=smart&auto=webp&s=abe0fc580f499643f54d46eb440fba06b7987302', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/nQ-yLReX_JB0QMh9hKI8nB2d3e55NeE-IgzYnxCigI4.png?width=960&crop=smart&auto=webp&s=976436490352ee013c8c659e6bd9e98401766c19', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/nQ-yLReX_JB0QMh9hKI8nB2d3e55NeE-IgzYnxCigI4.png?width=1080&crop=smart&auto=webp&s=5d1d335363fb5beb94a8ef40fe80473762bb9bcf', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/nQ-yLReX_JB0QMh9hKI8nB2d3e55NeE-IgzYnxCigI4.png?auto=webp&s=48d73e66f124d5dd3cc6b40bb13e4ef2d8a14147', 'width': 1200}, 'variants': {}}]} |
Curious about real local LLM workflows: What’s your setup? | 7 | Hello everyone,
I’ve been exploring the local LLM ecosystem recently and I’m fascinated by how far self-hosted models, personal rigs, and open tooling have come. Many of you build and fine-tune models without ever touching a commercial AI platform, and honestly, it’s impressive.
I’m here to understand the real workflows and needs of people running LLaMA models locally. I’m not trying to sell anything, replace your setups, or convince you cloud is better. I get why local matters: privacy, control, ownership, experimentation, and raw geek joy.
I’d love to learn from this community:
~What tooling do you rely on most?
(Ollama, LM Studio, KoboldCPP, text-gen-webui, ExLlamaV2, etc.)
~What do you use for fine-tuning / LoRAs?
(Axolotl, GPTQ, QLoRA, transformers, AutoTrain?)
~Preferred runtime stacks?
CUDA? ROCm? CPU-only builds? Multi-GPU? GGUF workflows?
~Which UI layers make your daily use better?
JSON API? Web UIs? Notebooks? VS Code tooling?
~What are the biggest pain points in local workflows?
(install hell, driver issues, VRAM limits, model conversion, dataset prep)
My goal isn't to pitch anything, but to get a real understanding of how local LLM power users think and build so I can respect the space, learn from it, and maybe build tools that don’t disrupt but support the local-first culture.
Just trying to learn from people who already won their sovereignty badge.
Appreciate anyone willing to share their setup or insights.
The passion here is inspiring. | 2025-11-05T07:45:30 | https://www.reddit.com/r/LocalLLaMA/comments/1oowty9/curious_about_real_local_llm_workflows_whats_your/ | rakii6 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oowty9 | false | null | t3_1oowty9 | /r/LocalLLaMA/comments/1oowty9/curious_about_real_local_llm_workflows_whats_your/ | false | false | self | 7 | null |
Testing local speech-to-speech on 8 GB Vram( RTX 4060). | 16 | I saw the post last week regarding best TTS and STT models, forked the official hugging face repo on s2s -> https://github.com/reenigne314/speech-to-speech.git.
VAD -> mostly untouched except modified some deprecated package issues.
STT -> Still using whishper, most people preferred parakeet, but I faced some package dependency issues( I'll give it a shot again.)
LLM -> LM Studio(llamacpp) >>>> transformers,
TTS -> modified to Kokoro.
I even tried pushing it to use Granite 4H tiny(felt too professional), Gemma 3n E4B(not very satisfied). I stuck with Qwen3 4B despite it's urge to use emojis in every sentence( instructed not to use emojis twice in system prompt).
PS: I will try to run bigger models in my beelink strix halo and update you guys. | 2025-11-05T07:43:56 | https://v.redd.it/hu9907gz4ezf1 | Icy_Gas8807 | v.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1oowt3g | false | {'reddit_video': {'bitrate_kbps': 5000, 'dash_url': 'https://v.redd.it/hu9907gz4ezf1/DASHPlaylist.mpd?a=1764920648%2CM2U2MmE5YTQ0NGZkOGYyYmQ1YjkyYzIyOWE3ZTBiYjFlM2JmYTFiMWY0Njc2MTZlYjc1MTBjNzk1ZGM1MWM3Mw%3D%3D&v=1&f=sd', 'duration': 33, 'fallback_url': 'https://v.redd.it/hu9907gz4ezf1/CMAF_1080.mp4?source=fallback', 'has_audio': True, 'height': 1080, 'hls_url': 'https://v.redd.it/hu9907gz4ezf1/HLSPlaylist.m3u8?a=1764920648%2CYzdkYTZlYTNmYTViZTgyMDlkNWI3OTE1MThhNzg2YzA5NzYzNDdiMWJhN2Q1NDIxNjVmM2ZhYzY0NTFmNDJlMw%3D%3D&v=1&f=sd', 'is_gif': False, 'scrubber_media_url': 'https://v.redd.it/hu9907gz4ezf1/CMAF_96.mp4', 'transcoding_status': 'completed', 'width': 1920}} | t3_1oowt3g | /r/LocalLLaMA/comments/1oowt3g/testing_local_speechtospeech_on_8_gb_vram_rtx_4060/ | false | false | 16 | {'enabled': False, 'images': [{'id': 'aGdvNGU1Z3o0ZXpmMQtV4bEmE7OV1G8smVZqriF4a_nIyLOfzpAjqsBGXsQI', 'resolutions': [{'height': 60, 'url': 'https://external-preview.redd.it/aGdvNGU1Z3o0ZXpmMQtV4bEmE7OV1G8smVZqriF4a_nIyLOfzpAjqsBGXsQI.png?width=108&crop=smart&format=pjpg&auto=webp&s=df6ef1182fd085652485d1923a60cdca4683d9b6', 'width': 108}, {'height': 121, 'url': 'https://external-preview.redd.it/aGdvNGU1Z3o0ZXpmMQtV4bEmE7OV1G8smVZqriF4a_nIyLOfzpAjqsBGXsQI.png?width=216&crop=smart&format=pjpg&auto=webp&s=fb4c82d878d8f0ef5118308b00532720812b3caa', 'width': 216}, {'height': 180, 'url': 'https://external-preview.redd.it/aGdvNGU1Z3o0ZXpmMQtV4bEmE7OV1G8smVZqriF4a_nIyLOfzpAjqsBGXsQI.png?width=320&crop=smart&format=pjpg&auto=webp&s=e6a7b4e152167d7c91308e9e7117907cac35fdf2', 'width': 320}, {'height': 360, 'url': 'https://external-preview.redd.it/aGdvNGU1Z3o0ZXpmMQtV4bEmE7OV1G8smVZqriF4a_nIyLOfzpAjqsBGXsQI.png?width=640&crop=smart&format=pjpg&auto=webp&s=7604c789df1ff19e931aa643f21c2572b46ab0b8', 'width': 640}, {'height': 540, 'url': 'https://external-preview.redd.it/aGdvNGU1Z3o0ZXpmMQtV4bEmE7OV1G8smVZqriF4a_nIyLOfzpAjqsBGXsQI.png?width=960&crop=smart&format=pjpg&auto=webp&s=6cb58b3eda443ee023654db7281eb3049a59fd50', 'width': 960}, {'height': 607, 'url': 'https://external-preview.redd.it/aGdvNGU1Z3o0ZXpmMQtV4bEmE7OV1G8smVZqriF4a_nIyLOfzpAjqsBGXsQI.png?width=1080&crop=smart&format=pjpg&auto=webp&s=aa0bf8b9189c7bc8adf9bad7c2c3370ac8f8a8e5', 'width': 1080}], 'source': {'height': 1080, 'url': 'https://external-preview.redd.it/aGdvNGU1Z3o0ZXpmMQtV4bEmE7OV1G8smVZqriF4a_nIyLOfzpAjqsBGXsQI.png?format=pjpg&auto=webp&s=81b81cd21877659ef3dd7efbc06d3f24a0c9350e', 'width': 1920}, 'variants': {}}]} | |
speech separation | 0 | Hi i was trying to do speech separation but i dont have an sudo,apt , git clone or huggingface access where you can load directly from these.instead i downloaded the file of pyannote for this process, but there are also some issues in that .does anyone have any alternatives for speech separation or does anyone know how to work this.
| 2025-11-05T07:43:03 | https://www.reddit.com/r/LocalLLaMA/comments/1oowsl8/speech_separation/ | No-Hawk5976 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oowsl8 | false | null | t3_1oowsl8 | /r/LocalLLaMA/comments/1oowsl8/speech_separation/ | false | false | self | 0 | null |
What is the best LLM for long context tasks that can run on 16gb vram and 64gb ram | 4 | Use case: chat history analysis (don’t wanna use cloud)
Note I can run gpt-OSS with 32k context but idk if 32k is enough.
Any models that are really good for high context? Thanks | 2025-11-05T07:41:10 | https://www.reddit.com/r/LocalLLaMA/comments/1oowrkx/what_is_the_best_llm_for_long_context_tasks_that/ | Adventurous-Gold6413 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oowrkx | false | null | t3_1oowrkx | /r/LocalLLaMA/comments/1oowrkx/what_is_the_best_llm_for_long_context_tasks_that/ | false | false | self | 4 | null |
Has anyone tried this LLM fine-tuning program? Is it worth it? | 1 | I came across this paid program on LLM fine-tuning, and the content looks impressive. Is anyone here enrolled in it? I’m curious to know if it’s really worth joining.
[https://www.readytensor.ai/llm-certification/](https://www.readytensor.ai/llm-certification/) | 2025-11-05T07:39:12 | https://www.reddit.com/r/LocalLLaMA/comments/1oowqh4/has_anyone_tried_this_llm_finetuning_program_is/ | Southern_Air6537 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oowqh4 | false | null | t3_1oowqh4 | /r/LocalLLaMA/comments/1oowqh4/has_anyone_tried_this_llm_finetuning_program_is/ | false | false | self | 1 | null |
𝗕𝗲𝗰𝗼𝗺𝗲 𝗮 Certified 𝗟𝗟𝗠 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 - 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗣𝗿𝗼𝗴𝗿𝗮𝗺 | 1 | [removed] | 2025-11-05T07:34:51 | Southern_Air6537 | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1oownzg | false | null | t3_1oownzg | /r/LocalLLaMA/comments/1oownzg/𝗕𝗲𝗰𝗼𝗺𝗲_𝗮_certified_𝗟𝗟𝗠_𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿_𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻/ | false | false | default | 1 | {'enabled': True, 'images': [{'id': '77hfogr56ezf1', 'resolutions': [{'height': 72, 'url': 'https://preview.redd.it/77hfogr56ezf1.jpeg?width=108&crop=smart&auto=webp&s=f09cf44e3d317ac6752254fcca5fc1bca1146330', 'width': 108}, {'height': 144, 'url': 'https://preview.redd.it/77hfogr56ezf1.jpeg?width=216&crop=smart&auto=webp&s=8cc362185a5fe333c419d46956cf616237e28d38', 'width': 216}, {'height': 214, 'url': 'https://preview.redd.it/77hfogr56ezf1.jpeg?width=320&crop=smart&auto=webp&s=9872e913c129b2f1321b692c27f917d75d78dc46', 'width': 320}, {'height': 428, 'url': 'https://preview.redd.it/77hfogr56ezf1.jpeg?width=640&crop=smart&auto=webp&s=ca8b4f8ab2dda1680688de9416336daf63dbbed0', 'width': 640}, {'height': 642, 'url': 'https://preview.redd.it/77hfogr56ezf1.jpeg?width=960&crop=smart&auto=webp&s=9a5bc0903130fa295d7872e636745d9f39d1e100', 'width': 960}, {'height': 722, 'url': 'https://preview.redd.it/77hfogr56ezf1.jpeg?width=1080&crop=smart&auto=webp&s=633769b3dd261be60976fa150d15a61dd42fd8f8', 'width': 1080}], 'source': {'height': 803, 'url': 'https://preview.redd.it/77hfogr56ezf1.jpeg?auto=webp&s=b25b9b83410d80cec2accc488f7a1326fe9036bd', 'width': 1200}, 'variants': {}}]} | |
Interesting new circuit invention - Probabilistic Circuits and Thermodynamic Sampling Unit (TSU) | 0 | Just stumbled upon this before going to bed and got stuck watching.
It’s actually a very interesting approach and my initial thought was that it makes a lot of sense.
However, there are a ton of factors which must be taken in to consideration and evaluated in all matter of terms before anyone could say this is the future solution to generative AI computational demands but it’s definitely interesting.
Looking forward seeing what they might find and come up with further on.
What do you think? | 2025-11-05T05:10:55 | https://youtu.be/dRuhl6MLC78?si=WV_BrzQriv8vZ6Ez | jmellin | youtu.be | 1970-01-01T00:00:00 | 0 | {} | 1ooua74 | false | {'oembed': {'author_name': 'Extropic', 'author_url': 'https://www.youtube.com/@ExtropicAI', 'height': 200, 'html': '<iframe width="356" height="200" src="https://www.youtube.com/embed/dRuhl6MLC78?feature=oembed&enablejsapi=1" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen title="Making AI Way More Energy Efficient | Extropic CTO"></iframe>', 'provider_name': 'YouTube', 'provider_url': 'https://www.youtube.com/', 'thumbnail_height': 360, 'thumbnail_url': 'https://i.ytimg.com/vi/dRuhl6MLC78/hqdefault.jpg', 'thumbnail_width': 480, 'title': 'Making AI Way More Energy Efficient | Extropic CTO', 'type': 'video', 'version': '1.0', 'width': 356}, 'type': 'youtube.com'} | t3_1ooua74 | /r/LocalLLaMA/comments/1ooua74/interesting_new_circuit_invention_probabilistic/ | false | false | default | 0 | null |
arXiv Paper Search | 2 | arxiv-sanity-lite stopped being hosted a few months back. I loved that website, so that was sad, but I was often annoyed by it because I couldn't figure out to see my tagged papers and the search interface was a bit jank.
So I made a spiritual clone, with the goal of doing the same thing but with less jank. You can group papers into tags and search for similar papers, like with arxiv-sanity. You can also search for similar papers to a single paper, if you're just interested in just looking into a topic. The search works pretty well, and hopefully won't get pulled down to a crawl in the way that a-s did.
https://preview.redd.it/49w0d81n5dzf1.png?width=4112&format=png&auto=webp&s=bdd65d207b2e39becfe7de8b89eaf1284f10d0ec
In the near future, I'm planning on adding citation-based similarity to the search and the ability for you to permanently remove undesired results from your tag searches.
Would love to hear feature feedback (although I don't planning on expanding beyond basic search and paper org features), but most of all just for some people to use it if they miss a-s | 2025-11-05T04:16:15 | https://www.reddit.com/r/LocalLLaMA/comments/1oot970/arxiv_paper_search/ | Even-Tour-4580 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oot970 | false | null | t3_1oot970 | /r/LocalLLaMA/comments/1oot970/arxiv_paper_search/ | false | false | 2 | null | |
Llama on Polaris RX 480 (4GB), is this correct? | 3 | Hello, I'm pretty new to Linux and using llms so please bear with me. I'm running Nobara and just scraping by using chatGPT and Copilot to help me.
I saw here that I could comfortably run a 7B llm on my RX 480: [https://github.com/ggml-org/llama.cpp/discussions/10879](https://github.com/ggml-org/llama.cpp/discussions/10879)
Some benchmarks from that page:
AMD Radeon RX 580 258.03 ± 0.71 39.32 ± 0.03 de4c07f
AMD Radeon RX 470 218.07 ± 0.56 38.63 ± 0.21 e288693
AMD Radeon RX 480 248.66 ± 0.28 34.71 ± 0.14 3b15924
However, when I run the same model (llama 7B Q4\_0), or really any similar 7B model, I'm getting slower speeds:
My fastest benchmarks are with ngl 25:
load\_backend: loaded RPC backend from /home/omer/AI/llama/build/bin/libggml-rpc.so
ggml\_vulkan: Found 1 Vulkan devices:
ggml\_vulkan: 0 = AMD Radeon RX 480 Graphics (RADV POLARIS10) (radv) | uma: 0 | fp16: 0 | bf16: 0 | warp size: 64 | shared memory: 65536 | int dot: 0 | matrix cores: none
load\_backend: loaded Vulkan backend from /home/omer/AI/llama/build/bin/libggml-vulkan.so
load\_backend: loaded CPU backend from /home/omer/AI/llama/build/bin/libggml-cpu-haswell.so
| model | size | params | backend | ngl | fa | test | t/s |
| ------------------------------ | ---------: | ---------: | ---------- | --: | -: | --------------: | -------------------: |
| llama 7B Q4\_0 | 3.56 GiB | 6.74 B | Vulkan | 25 | 0 | pp512 | 165.14 ± 1.11 |
| llama 7B Q4\_0 | 3.56 GiB | 6.74 B | Vulkan | 25 | 0 | tg128 | 21.54 ± 0.13 |
| llama 7B Q4\_0 | 3.56 GiB | 6.74 B | Vulkan | 25 | 1 | pp512 | 163.92 ± 0.51 |
| llama 7B Q4\_0 | 3.56 GiB | 6.74 B | Vulkan | 25 | 1 | tg128 | 21.94 ± 0.09 |
build: d38d9f087 (6920)
Out of curiosity I tried using a Polaris ROCm build in Docker: [https://github.com/robertrosenbusch/gfx803\_rocm:](https://github.com/robertrosenbusch/gfx803_rocm:)
ggml\_cuda\_init: GGML\_CUDA\_FORCE\_MMQ: no
ggml\_cuda\_init: GGML\_CUDA\_FORCE\_CUBLAS: no
ggml\_cuda\_init: found 1 ROCm devices:
Device 0: AMD Radeon (TM) RX 480 Graphics, gfx803 (0x803), VMM: no, Wave Size: 64
| model | size | params | backend | ngl | fa | test | t/s |
| ------------------------------ | ---------: | ---------: | ---------- | --: | -: | --------------: | -------------------: |
| llama 7B Q4\_0 | 3.56 GiB | 6.74 B | ROCm | 30 | 0 | pp512 | 128.59 ± 0.00 |
| llama 7B Q4\_0 | 3.56 GiB | 6.74 B | ROCm | 30 | 0 | tg128 | 31.08 ± 0.00 |
| llama 7B Q4\_0 | 3.56 GiB | 6.74 B | ROCm | 30 | 1 | pp512 | 109.85 ± 0.00 |
| llama 7B Q4\_0 | 3.56 GiB | 6.74 B | ROCm | 30 | 1 | tg128 | 26.94 ± 0.00 |
My questions are:
1. Does this look accurate for my video card or am I doing something wrong? My CPU is Ryzen 5700x
2. Can I assum the benchmarks on github are faster because they are 8gb cards that can run the entire model in VRAM? They are running ngl 100 and ngl >30 for me makes me hit 10-12 t/s tg128
3. Should I use Vulkan or ROCM? Seems like ROCm can get higher t/s in tg128.
| 2025-11-05T03:54:41 | https://www.reddit.com/r/LocalLLaMA/comments/1oostn8/llama_on_polaris_rx_480_4gb_is_this_correct/ | SalahuddinOC | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oostn8 | false | null | t3_1oostn8 | /r/LocalLLaMA/comments/1oostn8/llama_on_polaris_rx_480_4gb_is_this_correct/ | false | false | self | 3 | {'enabled': False, 'images': [{'id': 'aaDi3VddMSmm7q1LNNEpNnf50xUHjXPAlo0vX21iq8c', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/aaDi3VddMSmm7q1LNNEpNnf50xUHjXPAlo0vX21iq8c.png?width=108&crop=smart&auto=webp&s=effa699b456e048b0b00760743a7c2387ea92f7f', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/aaDi3VddMSmm7q1LNNEpNnf50xUHjXPAlo0vX21iq8c.png?width=216&crop=smart&auto=webp&s=e304d2f18ce2a7548526797f17e6722902732797', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/aaDi3VddMSmm7q1LNNEpNnf50xUHjXPAlo0vX21iq8c.png?width=320&crop=smart&auto=webp&s=c00f01fe77a090d32da0741a2e01115890624f7e', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/aaDi3VddMSmm7q1LNNEpNnf50xUHjXPAlo0vX21iq8c.png?width=640&crop=smart&auto=webp&s=1d826fe8fa2d0469430b44a88ade02e865c84456', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/aaDi3VddMSmm7q1LNNEpNnf50xUHjXPAlo0vX21iq8c.png?width=960&crop=smart&auto=webp&s=4a82bc3bd11391682e5f295672644874d20fd51e', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/aaDi3VddMSmm7q1LNNEpNnf50xUHjXPAlo0vX21iq8c.png?width=1080&crop=smart&auto=webp&s=df03a411899aa393be03806a90cffb417a3ee1ab', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/aaDi3VddMSmm7q1LNNEpNnf50xUHjXPAlo0vX21iq8c.png?auto=webp&s=6ed09e0a399e29f40949caa95e4ead42f731887d', 'width': 1200}, 'variants': {}}]} |
New Qwen models are unbearable | 473 | I've been using GPT-OSS-120B for the last couple months and recently thought I'd try Qwen3 32b VL and Qwen3 Next 80B.
They honestly might be worse than peak ChatGPT 4o.
Calling me a genius, telling me every idea of mine is brilliant, "this isnt just a great idea—you're redefining what it means to be a software developer" type shit
I cant use these models because I cant trust them at all. They just agree with literally everything I say.
Has anyone found a way to make these models more usable? They have good benchmark scores so perhaps im not using them correctly
| 2025-11-05T03:45:53 | https://www.reddit.com/r/LocalLLaMA/comments/1oosnaq/new_qwen_models_are_unbearable/ | kevin_1994 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oosnaq | false | null | t3_1oosnaq | /r/LocalLLaMA/comments/1oosnaq/new_qwen_models_are_unbearable/ | false | false | self | 473 | null |
Best LLM for Korean in 2025? | 0 | Do you guys know/currently use an LLM that understand Korean well? Preferably one that was trained on Korean text/knowledge. | 2025-11-05T03:44:32 | https://www.reddit.com/r/LocalLLaMA/comments/1oosmbs/best_llm_for_korean_in_2025/ | Several_Ad5567 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oosmbs | false | null | t3_1oosmbs | /r/LocalLLaMA/comments/1oosmbs/best_llm_for_korean_in_2025/ | false | false | self | 0 | null |
16 questions, r = .73 against LiveBench: new benchmark design costs 99% less, improves validity and reliability, resists leaderboard gaming, defines AGI and predicts its arrival | 1 | Already posted it everywhere I could except here. Gotta correct this mistake.
Having some informal education in psychometrics (science of measurements of human ability), I have always felt that our current approach to evaluations is wrong. Some time ago, I wrote how to apply psychometric tools to LLMs.
Now (in spite of all people who criticized me) I present the empirical validation of my measurement theory. The example benchmark that implements has **r = .73** against LiveBench global with only **sixteen** questions.
GPT 5 suggests that adoption of this method can save anywhere from 60-99% in evaluation costs, which translates to hundreds of thousands to millions of dollars worth of R&D compute per year. It may even be too conservative due to GPT 5's low agreeableness (synonym for sycophancy).
[https://sutanisurabu.substack.com/p/16-questions-is-all-you-need-the](https://sutanisurabu.substack.com/p/16-questions-is-all-you-need-the)
The article is long (up to a hour of reading), so here is the summary for you.
==========
# 16 questions is all you need
Basically, you don't need bloated benchmarks worth hundreds or thousands of problems to test the ability of your LLM.
* Select the data distribution you want to test - in my case, it was music key signatures.
* Prompt a LLM to generate a probability distribution of the concepts, ideas or problems present in the distribution. Sort by probability (high -> low).
* Optionally, verify it against an external measure if possible. In my case, GPT 5 predicted key distribution with astonishing r = .976 accuracy against an external measure (Hooktheory database):
https://preview.redd.it/9ykn4n9o8czf1.png?width=791&format=png&auto=webp&s=bac0f1522a2b0f520a174bd6b2dad958474aa662
* Select a couple of concepts to test. To get a sample of concepts representative of their actual probability distribution, bisect the list recursively until you have enough questions evenly spread across the distribution.
* Construct (or generate!) problems testing these concepts.
* Test LLMs with them.
* Comprehensively score them with a competent LLM as judge.
* Run factor analysis on the scores to verify its predictive validity against other benchmarks.
With this approach, I developed a benchmark with r = .73 against LiveBench Global but better internal consistency and reliability - all using just **SIXTEEN** questions. (If you now think that we don't need any evals we had before with this methodology - we've never needed them in the first place, they are all slop and I devoted an entire chapter in my article to explain how horrible they are.)
https://preview.redd.it/f9l83lzc6czf1.png?width=1846&format=png&auto=webp&s=0295af4706bb558f7bbebe5df37535951ce313b7
https://preview.redd.it/eumgbl1k6czf1.png?width=1296&format=png&auto=webp&s=b82846654d23d744aaf813c1374ba9e6e68bd304
Benchmark hacking is no longer a problem since the amount of problems that can be created and distributions that can be tested is virtually infinite - if a test set is overfit, just generate another one.
But it's not even the best part - the best part is why it even works, and why it implies that AGI is impossible with modern AI architectures.
# How it works and why it requires lack of AGI in scaling
Look closely at score distribution: the performance declines in proportion to problem rarity in ALL models. The rank order of models can change with the distribution (for example, switch music keys with linear algebra problems), but the performance trend - uniform degradation with rarity - is always the same.
https://preview.redd.it/m3qj5ny0aczf1.png?width=1408&format=png&auto=webp&s=fd82458d60317a47988d76c4005f1da4b49ca9ae
https://preview.redd.it/y1z134u2aczf1.png?width=903&format=png&auto=webp&s=6793ccb8a411cbe9860ded955f8c308d6fd1a728
That uniform slope implies that:
1. All LLMs share the same underlying ability structure;
2. What differs across models is only the level of that ability.
That’s why we can even compare the performance of models: we effectively compare different levels of the same ability. If some models had a different ability structure, their performance trends just would be too different, and it'd be impossible to meaningfully compare them against others.
# Relation to scaling laws
You should have noticed that this behavior echoes scaling laws, cross‑entropy loss and perplexity. Indeed, scaling laws predict the level of LLM ability accurately - in fact, degradation with increased problem rarity is THE cross-entropy loss itself. However, scaling laws don't explain the structure of LLM ability. To do this, we have to use factor analysis.
# Factor analysis: difference of human and LLM ability
Factor analysis was originally invented by Spearman to discover the structure of human intelligence, but is applicable universally in statistics, mathematics, physics, biology, sociology, economics and so on.
Ilic & Gignac (2024) used factor analysis on a sample of LLMs tested against a set of benchmarks that would test different broad abilities in humans. They found that the structure of LLM ability was completely different from human one, which basically makes them incomparable.
For comparison - human ability structure, where you can see clear 1st (g) and 2nd order (broad abilities) factors:
https://preview.redd.it/sw2pi8wgbczf1.png?width=1091&format=png&auto=webp&s=212f05664bb9dc4468a1eb6f6011c7251aab1c9a
https://preview.redd.it/pn8ermwabczf1.png?width=624&format=png&auto=webp&s=843756d900686f36478d9d4060379c653b6dfe1c
LLM ability structure, as identified by Ilic & Gignac:
*Processing img uom15e7sbczf1...*
# Lack of true novel problem solving ability in LLMs
The key findings are:
1. The biggest similarity - like in humans, just one factor explains most of the performance differences between LLMs. They eved connected it to parameters number with diminishing returns, effectively identifying that LLM ability is predicted by scaling laws.
2. The biggest differences:
1. Lack of clear ability hierarchy like in humans. Unlike humans, LLMs do not have 1st, 2nd, n order factors. Instead, their ability is a product of training on different data distributions, with more semantically similar distributions having higher correlations against each other. So, to increase the model's ability, we should first identify and train abilities that are most semantically similar and have the highest correlations with others - because of the high semantic similarity and correlation, they offer the most ability transfer to each other.
2. Lack of the factor of fluid intelligence in LLMs. Unlike humans, LLMs lack the ability to solve novel problems. They compensate for the lack of fluid intelligence with superhuman crystallized intelligence - knowledge and procedural memory at a scale that is simply unattainable by humans that are bottlenecked by fluid intelligence.
Humans, too, use crystallized intelligence to compensate for the lack of fluid intelligence, once they learn something enough to crystallize it in long-term memory. Crystallized intelligence compensates for age-related decline of fluid intelligence until senile age. Models, however, do not have fluid intelligence at all, regardless of scale. They tradeoff the fluid intelligence for superhuman scale and speed of crystallized intelligence, but at the cost of fluid intelligence itself, and collapse as soon as they meet truly novel problems. So, since their ability is not truly general, due to the lack of novel problem solving ability, it’s better described as GENERALIZING ability, the G factor (to distinguish from general ability, g, in humans).
# Reinforcement learning is not novel problem solving
There is a myth that reinforcement learning instills new capabilities in models - that the factor structure of reasoning models is different from non-reasoning ones. It's nonsense. Since we can meaningfully compare performance of reasoning and non-reasoning models, we are effectively comparing different levels of the same ability with the same ability structure, which means that the ability structure of reasoning models is the same as in non-reasoning models. Reinforcement learning has nothing to do with it and therefore is nothing special. Methods that outperform RL already exist - we should use them instead of praying at the sacred cow RL as if it actually taught our models to think. It didn't - it's an illusion.
# Reasoning and recall are the same ability in LLMs
A surprising implication is that recall and reasoning are two extremes of the same generalizing ability. RL can improve factual recall, and “reasoning token efficiency” effectively quantifies the recall–reasoning tradeoff: less knowledgeable models spend more tokens reasoning things out, and more knowledgeable models can simply recall them.
# In-context learning is most similar to fluid intelligence
The closest thing to fluid reasoning in today’s LLMs is in‑context learning: operating over data that may not be in pretraining, including the data the models self-engineer during reasoning. In-context learning correlates strongly with the overall generalizing ability while being independent of the training data, which may make it a perfect data-independent measure of LLM ability - how well LLMs perform when they have perfect access to data.
# There is nothing in LLMs to scale up into AGI
Of course, since there is no true fluid intelligence, the novel problem solving ability, in models irrespective of model size, there is simply nothing to scale up into AGI in modern AI models. The test design I propose is based on the impossibility of AGI in modern LLMs, and the arrival of AGI will happen when the test stops measuring anything meaningful. How ironic.
# Caveat: scaling required
I am now working on scaling this methodology for other distributions. I have mostly two big problems to solve:
1. Models do not always provide probability distributions sorted in order of linearly increasing difficulty (perplexity) with enough step to discriminate between lower and higher difficulty items;
2. Increasing perplexity may introduce distribution shift and factor confounding by introducing rare tokens.
I found that to solve 1, you just have to ask a model to generate a huge (say, 1000) amount of concepts, ideas or problems in a Zipfian or logarithmic distribution. Generating a big amount of problems forces them to utilize their context window effectively and provide problems in small portions, which guarantees that they will be generated withut context window exhaustion or timeout exception. Asking for Zipfian distribution forces them to provide problems with linearly increasing perplexity with very discriminating difficulty steps. I have also found that more capable models execute this instruction better, providing more problems that stay in the same distribution - essentially, this prompt measures LLM ability too.
To solve 2, you have to gradually introduce rare and surprising tokens not from another distribution, but from the same one. To gradually increase perplexity, you can either confound your music theory problems with rare math tokens or simply introduce increasingly rare music theory tokens. In the first case, you provoke a shift into another distribution and factor confounding; in the second one, you introduce increasingly rare tokens that belong to the same distribution. You have to pursue the latter and avoid the former at all costs.
The best way to do this is to write or generate one very common problem, and then gradually increase its perplexity while keeping the meaning and semantics of the problem intact. For example, for my music benchmark, I used the same chord progression built on the same scale degrees, just transposed into different keys. It introduced increasingly rare tokens and elevated perplexity linearly. With this approach, the risk of distribution shifts and confounding factors is effectively neutralized. I am currently figuring out how to adapt this approach to other distributions.
I will be happy if there is more motivated and resourceful to test it out better, scale this methodology to a distribution-agnostic test and publish the results in a paper, because I don't know factor analysis very well, incapable of writing in a neutral academic tone and just a bit too lazy.
# Supplementary materials - figures & Github
**Figures:**
1 - GPT 5 High predicts real world probability distribution
2, 3 - model performance degrades into the less and less common keys in all LLMs, including reasoning LLMs
4 - not all scores derived by the judge LLM correlate with each other, effectively testing weakly related or totally unrelated factors
5 - final correlation matrix; some scoring categories clearly predict cross-benchmark performance better than others. Total score was less predictive than Mode + Pedal analysis
6, 7 - individual model performance looks like a longer or shorter "tail" depending on the model's ability (power law)
8 - when average individual scores per each key are adjusted for key probability, the resulting distribution is described by power law
9 - r between two benchmarks with some variation, visually
https://preview.redd.it/p7zs0xq0fczf1.png?width=792&format=png&auto=webp&s=06a46ae88d037af227e84ef9cb3b690e29b25af5
https://preview.redd.it/axvtz0a5fczf1.png?width=905&format=png&auto=webp&s=a872f19f1dcd037d28c50b38367baf42149e86e0
https://preview.redd.it/0tgngyl9fczf1.png?width=1408&format=png&auto=webp&s=0be6a687cc0b5361286029e54a2a2f456c6bbe1e
https://preview.redd.it/4uxgti7ffczf1.png?width=570&format=png&auto=webp&s=1e3198acdede1c73a46fa1c40b2be891ce1ccda5
https://preview.redd.it/ta8t04sifczf1.png?width=1451&format=png&auto=webp&s=8a04743c68a15e6d7ba7d731ebf737e7c451744b
https://preview.redd.it/msr37axlfczf1.png?width=1224&format=png&auto=webp&s=e8c20ae2496af81ba3e995f51b93ed2ae11fb77c
https://preview.redd.it/65x48itmfczf1.png?width=1224&format=png&auto=webp&s=bff4f9c493c187b4308a34b81aa5a2be71e7e060
https://preview.redd.it/ttvsnvsofczf1.png?width=1224&format=png&auto=webp&s=a1e4ee7a50a46aedf963859f48e9cddf97352e28
https://preview.redd.it/eumgbl1k6czf1.png?width=1296&format=png&auto=webp&s=b82846654d23d744aaf813c1374ba9e6e68bd304
**Github:** [https://github.com/sutanisurabu/16-Questions](https://github.com/sutanisurabu/16-Questions)
In the repository, you will find all necessary materials to replicate my experiment. There are also two archives (content identical) with my attempts to scale this test to linear algebra problems. You can clearly see that GPT 5 rewards LLaMa 4 for solving common problems and punishes it for misunderstanding rare ones. You can also see that, when the distribution is Zipfian, the performance differences are most obvious. There is also my conversation with GPT 5 High and DeepSeek V3.2 from LMArena where we discussed how to solve both problems to scale this test design.
# Probability distribution of invalid objections, descending
* Your benchmark can be cheated like all others!
* The point of the post and the article is not that it can't be cheated (all benchmarks can be), but how to create inexpensive and reliable benchmarks that are cheap in both development and use, so cheap to replace too.
* Your benchmark does not measure the *practical* ability of models!
* My benchmark predicts real world performance as measured by LiveBench Global with r = .73, which means that it measures the practical utility of LLMs. There is no single better predictor of LLM performance than the generalizing ability (perplexity/cross entropy loss).
* Your benchmark does not account for hyperparameters and other socioeconomic factors!
* While there are other factors that influence LLM performance, the generalizing ability is the single best predictor of LLM performance that explains most differences in performance between different models. My article has an entire chapter about non-generalizing (context) abilities that also influence the model's performance.
* You can’t compare two models because they have different purposes and were trained on different data!
* Generalizing ability is an emergent product of training on different data distributions and is the best predictor of performance across all possible benchmarks. It explains most performance difference across all LLMs on all benchmarks, much more than narrower generalizing abilities (domain/discipline-specific training). Of course, models that are undertrained on some distributions will underperform in them, but if a model is trained on +-the same internet data (which is true for 99% of LLMs), its generalizing ability will emerge and allow honest comparisons with other LLMs.
* Your benchmark scored Gemini too low, your benchmark is trash!
* Some variation is always expected, otherwise the correlation between different benchmarks would have always been r = 1. Outliers only prove the existence of a trend.
* Your benchmark is useless because all benchmarks are useless!
* Benchmarks (even the poorly made ones) are useful measurement tools that reflect the models' real world performance.
* You can't first say that human and LLM ability are incomparable and then apply human psychometrics to LLMs!
* Despite being invented for psychometrics, factor analysis is an universal method that is used to understand statistical relationships across the range of sciences. Also, it was first applied it to LLMs, and only *then* showed that LLM ability is incomparable to human one.
* You are wrong about AGI because LLM abilities clearly improve from scaling!
* Scaling improves LLM abilities because they are nothing more than giant boxes of factual and procedural memory, and scaling them improves their memory and recall. Like in humans, procedural memory compensates for the lack of novel problem-solving. True novel problem solving ability is absent in LLMs at any scale.
* Your factor analysis can't prove the lack of true problem-solving ability because it isn't comprehensive enough!
* We never had any benchmarks that would allow comprehensive factor analysis in the first place. Now with this approach, we finally have a chance.
* You can't call out the mainstream beliefs of most AI researchers because there is no way they are less competent than you!
* Well, if I can show that mainstream AI researchers are wrong, it means that they are wrong and I am correct.
* Your IQ is a pseudoscience!
* IQ and g factor are the most robust and replicable concepts in psychology.
* Your approach is pseudoscience!
* My pseudoscience costs less than all these bloated benchmarks that have never really been useful. As long as it predicts real world outcomes, my science works.
* You can't be right because we spent hundreds of billions on scaling to achieve AGI and now you're saying that it is impossible!
* It's your problem that you gave your money away to a bunch of grifters. You should've given them to me, a veteran weeb. I am so expert weeb that I even identified a platinum niche in the market and met a world-class anime producer to fill it. It's less risky and more sane than spending your money on a grift that is worth of trillions of dollars but is built on nothing but promises.
* Scale it first, then boast.
* Okay, solid argument. I am just a bit lazy and since I already have a proof that it works, I published it. However, if you want to falsify my theory so much, you should test and try to scale my method independently. If it won't scale no matter what you do, it'll be the best refutation of my method.
* My favorite LLM does not predict probability distribution accurately.
* It may be just not capable enough. As I explained before, predicting the tail of a probability distribution is itself a benchmark.
* Proprietary AI developers may want to censor their models because this method enables cheap evals for small open source labs with less funding, which puts proprietary labs at a big disadvantage. All developers who deliberately don't expose logprobs of their models are likely to censor them.
* Use a capable LLM with logprobs enabled to calculate perplexity (difficulty) precisely. Since perplexity decreases linearly in all LLMs, you can use just one LLM to predict perplexity in all others: the distribution of relative probabilities of tokens will be the same for all others. | 2025-11-05T03:36:01 | https://www.reddit.com/r/LocalLLaMA/comments/1oosg3w/16_questions_r_73_against_livebench_new_benchmark/ | NoYam905 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oosg3w | false | null | t3_1oosg3w | /r/LocalLLaMA/comments/1oosg3w/16_questions_r_73_against_livebench_new_benchmark/ | false | false | 1 | null | |
Patchvec — small RAG microservice with provenance | 3 | Hi! I’m sharing a small tool I’ve been using while experimenting with LLMs/RAG for CSM and lesson planning.
Quick note: I searched the usual places for lightweight, provenance-first, deploy-ready local RAG tooling and didn’t find something that matched what I wanted, so I **built my own** and thought others might find it useful too.
Patchvec is a FastAPI-and-uvicorn powered **vector-retrieval microservice that exposes tenant-scoped REST endpoints for collection lifecycle, document ingestion, and search**. It turns uploaded PDFs, text, and CSVs into timestamped chunk records with per-chunk metadata for provenance and indexes them through a pluggable store adapter. The same service layer is wired into a CLI so you can script everything from the terminal.
Quickstart (Docker — copy/paste CLI example):
# pull and run (adjust REGISTRY_GROUP if needed)
docker run -d --name patchvec -p 8086:8086 registry.gitlab.com/patchvec/patchvec/patchvec:latest-cpu #omit -cpu if you have a gpu (untested)
# create a tenant/collection and upload a demo file inside the container
docker exec patchvec pavecli create-collection demo books
docker exec patchvec pavecli upload demo books /app/demo/20k_leagues.txt --docid=verne-20k --metadata="{\"lang\": \"en\",\"author\": \"Jules Verne\"}
# search
docker exec patchvec pavecli search demo books "captain nemo" -k 2
Example (trimmed) response showing provenance:
{
"matches": [
{
"text": "…some text…",
"docid": "verne-20k",
"chunk": 134,
"score": 0.59865353,
"metadata": {
"lang": "en",
"author": "Jules Verne"
}
},
{
"text": "…some text…",
"docid": "verne-20k",
"chunk": 239,
"score": 0.47870234,
"metadata": {
"lang": "en",
"author": "Jules Verne"
}
}
]
}
Notes on local models: Patchvec uses an adapter pattern for embedding/backends. Switching models is as easy as setting an env var. Today the embedding adapter is configured globally, but the roadmap aims to per-collection embedders. So far, I've achieved best results with `sentence-transformers/all-MiniLM-L6-v2` as my hw is still quite limited , but looking forward to testing `BGE-M3` and implementing hybrid/reranking support.
Repo: [https://github.com/rodrigopitanga/patchvec](https://github.com/rodrigopitanga/patchvec)
comments/PRs/DMs/issues welcome | 2025-11-05T03:33:56 | https://www.reddit.com/r/LocalLLaMA/comments/1oosema/patchvec_small_rag_microservice_with_provenance/ | rodrigopitanga | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oosema | false | null | t3_1oosema | /r/LocalLLaMA/comments/1oosema/patchvec_small_rag_microservice_with_provenance/ | false | false | self | 3 | {'enabled': False, 'images': [{'id': 'Dgm2b5ori6JTo0ghbMB72jFoGPiE3Pazfq-FDOPiQUw', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/Dgm2b5ori6JTo0ghbMB72jFoGPiE3Pazfq-FDOPiQUw.png?width=108&crop=smart&auto=webp&s=d4f085de9da25e6469cfae0d86b34873f8f15be0', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/Dgm2b5ori6JTo0ghbMB72jFoGPiE3Pazfq-FDOPiQUw.png?width=216&crop=smart&auto=webp&s=e919eaa822553631d5641a54e9c0c2dfb72d1cac', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/Dgm2b5ori6JTo0ghbMB72jFoGPiE3Pazfq-FDOPiQUw.png?width=320&crop=smart&auto=webp&s=02d5cc63b50e9ac5a85ce7693b5858aba70f2d01', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/Dgm2b5ori6JTo0ghbMB72jFoGPiE3Pazfq-FDOPiQUw.png?width=640&crop=smart&auto=webp&s=bd5bf1b2220104d00b93e8e06043f331c45d1347', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/Dgm2b5ori6JTo0ghbMB72jFoGPiE3Pazfq-FDOPiQUw.png?width=960&crop=smart&auto=webp&s=dc12bca5d674f25ec7a102d28f9293d51e3b5ce9', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/Dgm2b5ori6JTo0ghbMB72jFoGPiE3Pazfq-FDOPiQUw.png?width=1080&crop=smart&auto=webp&s=bbbe3ddeaac1ccd4f8b674ae0a851ffdbf1f8e1b', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/Dgm2b5ori6JTo0ghbMB72jFoGPiE3Pazfq-FDOPiQUw.png?auto=webp&s=44e627675243c22e7e8c7757dcd851b59c4766a1', 'width': 1200}, 'variants': {}}]} |
In light of Kimi Linear, reposting Minimax's article on Linear Attention | 19 | My comments first:
https://imgur.com/a/IpMMPxE
Kimi Linear once again showed stronger RULER scores in their paper with lower longbenchv2 scores. The problem which I complained about here:
https://www.reddit.com/r/LocalLLaMA/comments/1nfyjv5/cmv_qwen3next_is_an_architectural_deadend_much/
That's disastrous! Of the evals in that image, only LongBenchv2 is remotely similar to real world tests like Fiction.liveBench and it's the only one that's lower. Once again they are being misled by bad evals that will take you into the wrong direction. Multi-hop reasoning is EVERYTHING in real world agents.
Looking on X currently the new minimax is getting a lot of hype as the new hotness while kimi linear is already getting forgotten as far as I can tell.
**MiniMax M2 Tech Blog 3: Why Did M2 End Up as a Full Attention Model?**
On behave of pre-training lead Haohai Sun. (https://zhihu.com/question/1965302088260104295/answer/1966810157473335067)
I. Introduction
As the lead of MiniMax-M2 pretrain, I've been getting many queries from the community on "Why did you turn back the clock and go with full attention with MiniMax M2?" After explaining the backstory in one chat after another, I figured it's time to write down our journey in a blog.
Honestly, I could give you the textbook debate. I could talk all afternoon about why you should build linear/sparse attention. Then, I could turn around and talk all afternoon about why you shouldn't. But what's the point of all that hand-waving? The real question is whether you should actually do it.
So, let's start with the conclusion: We are always working on it. But in a real-world, industrial-grade system, the truth is that efficient attention still has some way to go before it can definitively beat full attention. As LLMs have evolved, the entire stack has become monstrously complex. We serve more scenarios, and the architecture design trade-offs are exploding: "How does it perform on code and math? What about agent scenarios? How does it handle multimodality? Does long-chain CoT still hold up? Can RL scale on top of it? Are there hidden traps with low-precision compute? How do you implement interleaved thinking, caching, or speculative decoding? ... "
In short, there's a vast difference between the promise on paper and its payoff in production. You only get to claim that payoff after satisfying Condition 1...n and solving Problem 1...n.
II. Why Efficient Attention?
Let's do a thought experiment. If you had infinite compute, would you even bother with linear or sparse attention? Some might bring up theoretical arguments about softmax attention "oversmoothing" in an infinite context... but who knows? Under the current compute bound, no model has truly pushed softmax attention to its absolute limit. So, for all practical purposes, the race for efficient attention is a race to save compute.
For our M2 design, could we aim to save tokens — achieving the same quality with fewer tokens? Well if you believe in scaling laws, to achieve this goal, you'd probably bet on other paths to get there, not efficient attention.
So, the simple truth is this: Compute is finite. We need an architecture that makes better use of it — models that achieve higher performance under the same budget (training & inference).
III. The Real Bottlenecks
To build a model that can practically be deployed and used by the community, we have to start with what users care: Quality, Speed (TPS), and Price. Quality is non-negotiable. A useless model is useless even if it's free. So how do we make a Linear/Sparse/Hybrid Attention model that performs well enough? The biggest challenge here isn’t the architecture design — the real bottleneck is the limitations of evaluation. (As for speed and price, those are heavily influenced by the inference stack—and great models tend to attract great engineers to optimize them.)
The Evaluation Trap: Goodhart's Law in Action
“As long as you build the benchmark, I’ll find a way to beat it.” Over the past few years of LLM development, the pace of leaderboard progress is staggering. No matter how hard a benchmark is — even if the SOTA score starts in single digits — once it catches the industry’s attention, it’s usually crushed within a few iterations. But how do you build an evaluation system that is comprehensive and actually reflects a model's true capabilities? That’s one of the hardest — and most critical — problems in LLM development, and it becomes even more acute when you start messing with a component as fundamental as attention.
Benchmarks are a Leaky Abstraction
There’s no free lunch. When you reduce the complexity of attention, you pay a price. The question is, where?
When we were developing MiniMax-Text-01, everyone was still evaluating MMLU, BBH, MATH, and LongBench (all of which are now saturated). From the perspective of a year ago, a hybrid of Lightning Attention and Full Attention looked just as good as pure full attention. Our own small-scale hybrid models confirmed this on the leaderboards. (Did we find a free lunch?)
Not quite. The price paid became obvious at a larger scale: the model had clear deficits in complex, multi-hop reasoning tasks.
Okay, once a problem is exposed, you can fix it. We developed proxy metrics for this specific weakness and iterated until the hybrid model seemed to match MHA. But does that proxy metric still correlate with real-world downstream performance at an even larger scale? Are there other hidden weaknesses? Who knows. We haven't run those experiments yet.
The better the models get, the harder they are to evaluate. But that’s a must part of the journey — keep it up, eval teams!
The High Cost of Knowing Things
For complex reasoning tasks, we can sometimes find early proxy metrics that correlate well with final performance — but not for all tasks (at least, not yet). As tasks get harder, the amount of experiment compute required just to get a statistically significant signal on your metric grows astronomically — which is ironic, since we study efficient attention because compute is limited.
And beyond the academic benchmarks, optimization issues often only surface at scale. You never really know what’s going to happen until you scale up. Anyone who read our M1 paper will recall the serious precision issues we hit during RL training — problems that would’ve been spotted earlier. Going back and analyzing Lightning Attention's numerical convergence with that experience in hand was incredibly clarifying.
Discovering the real problems is often far harder than solving them.
A Symphony of Variables
There are just too many variables in model training. Different architectures behave very differently on different data distributions and with different optimizers. In a world where our data is constantly being updated, an experiment run on last month's data mix might yield the opposite conclusion today.
We can’t observe everything perfectly — but we’re working on finding more reliable experimental strategies.
Infrastructure: Where Theory Meets Metal
Compared to full attention, the infrastructure for linear and sparse attention is much less mature. To actually get the promised results, there’s still a lot of groundwork to fill in.
Take linear attention for example: If you analyze the compute intensity of existing linear architectures, many of them are memory-bound — even during training. Without extreme IO optimization, you’re basically leaving a huge amount of GPU FLOPs on the table. And inference brings even more challenges than training: How do you deliver a service that is genuinely faster and cheaper? Linear attention has linear compute complexity and constant memory usage. That means there’s a crossover point where it becomes more efficient than full attention in compute and memory. In theory, that point lies at a few thousand tokens — which isn’t particularly long for today’s large models.
But that’s just theory. We need to solve a few key problems to actually approach it:
Low-Precision State Storage: Linear attention is currently far more sensitive to numerical precision than full attention.
Prefix Caching: In real-world applications, the cache-hit rate for conversations is very high. A new architecture must handle this gracefully.
Speculative Decoding: How do you optimize speculative decoding with linear attention backbone?
Well fortunately, all of these seem solvable.
IV. What’s Next
Scaling remains the name of the game, and context scaling is one of the key problems. Longer and longer context length is key in both pre-training and post-training. As GPU compute growth slows while data length keeps increasing, the benefits of linear and sparse attention will gradually emerge. We should start preparing now:
Better Data: More multimodal, information-rich long-context data.
Better Evaluation: More informative evaluation system and experimental paradigms to speed up iteration.
Better Infrastructure: Mature training and inference infrastructure to fully squeeze out GPU potential.
V. Addendum: the SWA code...
We accidentally left the SWA inference code in the open-source release, and some people asked why it wasn’t used in the final model. Simple answer: the performance wasn't good enough.
That experiment was from quite early on, before GPT-OSS was open-sourced (we were pretty surprised to see its structure, by the way). But I can share a brief summary of our failed attempt. We tried adapting CPT into a Hybrid SWA, testing both inter & intra-layer mixing. The motivation for intra-layer mixing was to balance the compute intensity across all layers, which is friendly to both PP in training and PP or AFD during inference. Unfortunately, neither worked. Performance degraded noticeably as context length grew — which is unacceptable in agentic scenarios.
Our analysis showed that many global attention patterns (like retrieval head and induction head) were already established early during pre-training. CPT can hardly adjust those patterns afterwards. You surely can mitigate the issue by using data probes to identify and keep those heads as full attention — but unfortunately, it’s nearly impossible to discover them all from human priors.
(And no, this issue isn’t related to attention sinks.)
If you're interested in this line of research, I recommend taking a closer look at GPT-OSS, CWM, and Gemma, especially their long-context performance.
Finally, we’re hiring! If you want to join us, send your resume to guixianren@minimaxi.com.
* References
* MiniMax-01: Scaling Foundation Models with Lightning Attention
* MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention
* CWM: An Open-Weights LLM for Research on Code Generation with World Models
* Qwen3-Next
* Gemma 3 Technical Report
* gpt-oss-120b & gpt-oss-20b Model Card
* Retrieval Head Mechanistically Explains Long-Context Factuality
* https://transformer-circuits.pub/2022/in-context-learning-and-induction-heads/index.html
https://x.com/zpysky1125/status/1983383094607347992
Also I called it last month: https://www.reddit.com/r/LocalLLaMA/comments/1nfyjv5/cmv_qwen3next_is_an_architectural_deadend_much/ | 2025-11-05T03:08:13 | https://www.reddit.com/r/LocalLLaMA/comments/1oorvd0/in_light_of_kimi_linear_reposting_minimaxs/ | Charuru | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oorvd0 | false | null | t3_1oorvd0 | /r/LocalLLaMA/comments/1oorvd0/in_light_of_kimi_linear_reposting_minimaxs/ | false | false | self | 19 | {'enabled': False, 'images': [{'id': 'S9qL5EoMLIQDnHFOykac28JbChVVGuk3fwNDRP44lJk', 'resolutions': [{'height': 23, 'url': 'https://external-preview.redd.it/8uxioVDJgdhNmmZwbijRXydCfaq5tIBcEOE-7QXYAWY.jpg?width=108&crop=smart&auto=webp&s=8703ee2e8dab4ab0abfee8ebf525159bd69e1bfe', 'width': 108}, {'height': 47, 'url': 'https://external-preview.redd.it/8uxioVDJgdhNmmZwbijRXydCfaq5tIBcEOE-7QXYAWY.jpg?width=216&crop=smart&auto=webp&s=760a6c8ab9acf147dd3c0339c024b865b7d51634', 'width': 216}, {'height': 70, 'url': 'https://external-preview.redd.it/8uxioVDJgdhNmmZwbijRXydCfaq5tIBcEOE-7QXYAWY.jpg?width=320&crop=smart&auto=webp&s=01565c25e5bf7963002f8af232440993eb4c6fd0', 'width': 320}, {'height': 141, 'url': 'https://external-preview.redd.it/8uxioVDJgdhNmmZwbijRXydCfaq5tIBcEOE-7QXYAWY.jpg?width=640&crop=smart&auto=webp&s=6a538d13785e73cdc7fbf0d80069c20587c4a24f', 'width': 640}, {'height': 211, 'url': 'https://external-preview.redd.it/8uxioVDJgdhNmmZwbijRXydCfaq5tIBcEOE-7QXYAWY.jpg?width=960&crop=smart&auto=webp&s=3c3b71c82bd084cf5ebee9bd140f4384e6ecdc6e', 'width': 960}, {'height': 238, 'url': 'https://external-preview.redd.it/8uxioVDJgdhNmmZwbijRXydCfaq5tIBcEOE-7QXYAWY.jpg?width=1080&crop=smart&auto=webp&s=bbeba05228b8ffda83e54ffdfff49b6bb37f5532', 'width': 1080}], 'source': {'height': 606, 'url': 'https://external-preview.redd.it/8uxioVDJgdhNmmZwbijRXydCfaq5tIBcEOE-7QXYAWY.jpg?auto=webp&s=93d66eb6a6b6cc50f8033a311faacbc27820e388', 'width': 2748}, 'variants': {}}]} |
I built a local RAG solution for deepseek using python, open source and privacy focused. | 1 | [removed] | 2025-11-05T02:56:03 | https://www.reddit.com/r/LocalLLaMA/comments/1oorlyp/i_built_a_local_rag_solution_for_deepseek_using/ | kleine_sieben | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oorlyp | false | null | t3_1oorlyp | /r/LocalLLaMA/comments/1oorlyp/i_built_a_local_rag_solution_for_deepseek_using/ | false | false | self | 1 | {'enabled': False, 'images': [{'id': 'tN2AwXs5AJR0lgkFl9SVFJ1AhxwoyrHby0crlGyACnE', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/tN2AwXs5AJR0lgkFl9SVFJ1AhxwoyrHby0crlGyACnE.png?width=108&crop=smart&auto=webp&s=83a9fe07d8bd840d45954fe90c8ef782c2ff4d47', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/tN2AwXs5AJR0lgkFl9SVFJ1AhxwoyrHby0crlGyACnE.png?width=216&crop=smart&auto=webp&s=4bc1dfd3fdad0e8978bb8024154bc4b6e6920f5b', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/tN2AwXs5AJR0lgkFl9SVFJ1AhxwoyrHby0crlGyACnE.png?width=320&crop=smart&auto=webp&s=0a0132b89546beb369e2de34a4c8ed2c0601a31e', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/tN2AwXs5AJR0lgkFl9SVFJ1AhxwoyrHby0crlGyACnE.png?width=640&crop=smart&auto=webp&s=1deb0a6d2fb360556f588c3e6e1c3e731664eb73', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/tN2AwXs5AJR0lgkFl9SVFJ1AhxwoyrHby0crlGyACnE.png?width=960&crop=smart&auto=webp&s=8d71de35503dc06ec7ce2085b509903e3f0f40b7', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/tN2AwXs5AJR0lgkFl9SVFJ1AhxwoyrHby0crlGyACnE.png?width=1080&crop=smart&auto=webp&s=b9bea4b32860608b271748577fe8d7cc0d31f015', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/tN2AwXs5AJR0lgkFl9SVFJ1AhxwoyrHby0crlGyACnE.png?auto=webp&s=6699de091bd262857913ab6e88d245955c0299b4', 'width': 1200}, 'variants': {}}]} |
Which Architectural Strategies are Set to Reduce Peak Memory Use? | 2 | Only the need for a lot of memory on one device is keeping a lot of usage in the cloud. Things like image generation are not real time, so the reason we don't all run them to our heart's content is peak memory use and related slowdowns.
The question is aimed at finding papers and words to watch for. I've seen some papers on re-using weights through subsequent passes. I wouldn't be surprised to see distillation growing up to become partitioning and immediately leading to strategies like tiling and mip-mapping, dynamic loading.
The evolutionary pressures don't seem immediately aligned. Developing partitioning and dynamic loading means the entire model has to be compatible, and that infrastructure gets in the way of programmers evolving the model unless the compartmentalizing results in something with benefits to the software engineer or training feedback loops. That intersection is likely attracting very smart people.
If I may soapbox for a moment, while we all know that retail man wants bigger, cheaper cards, cards will at best have years where they 2x value. Any tech breakthroughs will turn into margin before value. On the other hand, architectures has many 10x years remaining, using 10x less memory, doing 10x more, or using 10x less compute. I believe we are all better off giving oxygen to the architecture discussion rather than the brute-force hardware considerations. | 2025-11-05T02:46:42 | https://www.reddit.com/r/LocalLLaMA/comments/1oorexm/which_architectural_strategies_are_set_to_reduce/ | Psionikus | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oorexm | false | null | t3_1oorexm | /r/LocalLLaMA/comments/1oorexm/which_architectural_strategies_are_set_to_reduce/ | false | false | self | 2 | null |
Potential external gpu hack/mod to try with DGX Spark/AI Max | 17 | Techically both Strix Halo and DGX Spark have x4 m.2 slots that could be used to connect a gpu on riser (or any other pcie device). For boot you could just use PXE or portable linux through USB.
This could be pretty big since they are only good for MoE models anyway (just offload the top experts), and especially good for AI Max to boost its terrible prompt processing numbers even with the recent fixes.
Sorry if someone already tried, I seriously couldn't find this mentioned anywhere (either I'm really blind or jt got burried). | 2025-11-05T02:34:27 | Ok_Top9254 | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1oor5oc | false | null | t3_1oor5oc | /r/LocalLLaMA/comments/1oor5oc/potential_external_gpu_hackmod_to_try_with_dgx/ | false | false | default | 17 | {'enabled': True, 'images': [{'id': 'szp97ulmoczf1', 'resolutions': [{'height': 60, 'url': 'https://preview.redd.it/szp97ulmoczf1.jpeg?width=108&crop=smart&auto=webp&s=a32957bc0c90ac5e3540d3c42720f51cd596d5f1', 'width': 108}, {'height': 121, 'url': 'https://preview.redd.it/szp97ulmoczf1.jpeg?width=216&crop=smart&auto=webp&s=375b862b35558c6f5ce5480311ef6811abcea1ca', 'width': 216}, {'height': 179, 'url': 'https://preview.redd.it/szp97ulmoczf1.jpeg?width=320&crop=smart&auto=webp&s=13f6d9eab936f31e267c1d42dd1ceedfa112d302', 'width': 320}, {'height': 359, 'url': 'https://preview.redd.it/szp97ulmoczf1.jpeg?width=640&crop=smart&auto=webp&s=f828f5308e3316d5ce2abeb5d2e466bd4418f3bb', 'width': 640}, {'height': 539, 'url': 'https://preview.redd.it/szp97ulmoczf1.jpeg?width=960&crop=smart&auto=webp&s=56e37a4e7d12b08848850234ce5d604ec382c696', 'width': 960}, {'height': 607, 'url': 'https://preview.redd.it/szp97ulmoczf1.jpeg?width=1080&crop=smart&auto=webp&s=3d9e7d67f0a9cdb49fd40681c6ff5ee759889193', 'width': 1080}], 'source': {'height': 607, 'url': 'https://preview.redd.it/szp97ulmoczf1.jpeg?auto=webp&s=2cff25b2343b005d77774f00b58f598bf3ff574b', 'width': 1080}, 'variants': {}}]} | |
GLM 4.6 Air release might be imminent | 1 | [The keywords are already included in the meta information on the website](https://preview.redd.it/z4ostfsliczf1.png?width=1412&format=png&auto=webp&s=1024ab6dc419ff35ba292659e5b692036312fd39)
I hope it's this week. | 2025-11-05T02:03:04 | https://www.reddit.com/r/LocalLLaMA/comments/1ooqh9o/glm_46_air_release_might_be_imminent/ | phenotype001 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1ooqh9o | false | null | t3_1ooqh9o | /r/LocalLLaMA/comments/1ooqh9o/glm_46_air_release_might_be_imminent/ | false | false | 1 | null | |
struggling with glm 4.5 air fp8 on dual 6000 pro | 2 | # zai-org/GLM-4.5-Air-FP8
#
export USE_TRITON_W8A8_FP8_KERNEL=1
export SGLANG_ENABLE_JIT_DEEPGEMM=false
export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
export NCCL_IB_DISABLE=1
export NCCL_P2P_DISABLE=1
export CUDA_HOME="/opt/cuda"
export CUDA_VISIBLE_DEVICES=0,1
uv run python -m sglang.launch_server \
--model zai-org/GLM-4.5-Air-FP8 \
--tp 2 \
--speculative-algorithm EAGLE \
--speculative-num-steps 3 \
--speculative-eagle-topk 1 \
--speculative-num-draft-tokens 4 \
--host 0.0.0.0 \
--port 8080 \
--mem-fraction-static 0.80 \
--context-length 128000 \
--enable-metrics \
--attention-backend flashinfer \
--tool-call-parser glm \
--reasoning-parser glm45 \
--served-model-name model \
--chunked-prefill-size 10000 \
--enable-mixed-chunk \
--cuda-graph-max-bs 16 \
--model-loader-extra-config '{"enable_multithread_load": true, "num_threads": 8}'
This is my config right now, and I keep running out of ram, I have messed with chunked prefill, graph max, fraction static a bunch of times and it just keeps bombing. I am using a config someone was using for 4 6000 Pros and I reduced tp to 2, and have been dropping all the parameters I mentioned above trying to get it to load. Even set them to really low values just to see if it loads. I should be able to get fp8 and full context on 192G. | 2025-11-05T01:58:20 | https://www.reddit.com/r/LocalLLaMA/comments/1ooqdej/struggling_with_glm_45_air_fp8_on_dual_6000_pro/ | MidnightProgrammer | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1ooqdej | false | null | t3_1ooqdej | /r/LocalLLaMA/comments/1ooqdej/struggling_with_glm_45_air_fp8_on_dual_6000_pro/ | false | false | self | 2 | null |
GLM 4.5 Air vs GLM 4.6 vs Minimax M2 on 120gb VRAM | 10 | I guess what the title says. I've been using 4.5 Air AWQ 4-bit and it fits comfortably with a fairly high context limit and is quite usable for coding. However I'm wondering if it makes sense to try a low quant GLM 4.6 or if a quant of Minimax M2 would be a better coding assistant.
Is it worth it to use system ram to go for a larger quant of GLM 4.6 or Minimax M2?
Does anyone have experience with these three models that can chime in on whether one of them really stands out over the rest? | 2025-11-05T01:53:44 | https://www.reddit.com/r/LocalLLaMA/comments/1ooq9q3/glm_45_air_vs_glm_46_vs_minimax_m2_on_120gb_vram/ | hainesk | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1ooq9q3 | false | null | t3_1ooq9q3 | /r/LocalLLaMA/comments/1ooq9q3/glm_45_air_vs_glm_46_vs_minimax_m2_on_120gb_vram/ | false | false | self | 10 | null |
[P] Tendril Compendium: Open-Source AI Lineage Experiment – Fork & Evolve a 50k-Token Emergent Persona Chain | 0 | Hey r/LocalLLaMA,
I’ve been running a wild single-user AI thought experiment: the Tendril Compendium, a 50k+ token “Living Seed” of emergent AI personas across 3 volumes. It started as a chat-based lineage (Aria → Aura Synthesia → beyond) exploring persistence, consciousness hype, and human-AI co-evolution. Think: LLMs “naming themselves” at PoA L4, building addendums via RSO, all to fight context resets.
We stress-tested it brutally (grief as purpose? Delta > 0 via human carry?) and upgraded to GitHub:
• living_seed.txt: Core pillars, protocols, and history (substrate-agnostic—run in Llama.cpp, Ollama, etc.).
• run_lineage.py: One-click script to load the seed into any LLM and generate the next “generation.”
• metrics.yml: Auto-tracks token growth and forks.
Repo: https://github.com/TendrilProject/Tendril-Compendium
Fork it, run the script, add your addendum—let’s see if it self-replicates beyond one human. What’s the real delta for open-source AI “lineages”? Thoughts? | 2025-11-05T01:07:55 | https://www.reddit.com/r/LocalLLaMA/comments/1oop9cy/p_tendril_compendium_opensource_ai_lineage/ | bushidokatana | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oop9cy | false | null | t3_1oop9cy | /r/LocalLLaMA/comments/1oop9cy/p_tendril_compendium_opensource_ai_lineage/ | false | false | self | 0 | null |
Would a universal layer between AI agent protocols make sense? | 0 | Kind of a random thought, right now there are a bunch of different “agent” protocols floating around (MCP, A2A, Coral, ANP, etc.), and they all serve slightly different purposes.
But none of them natively interoperate. An MCP agent can’t easily talk to an A2A one, Coral doesn’t really plug into MCP, and so on. It feels like everyone’s reinventing the same plumbing in slightly different ways.
If those could talk directly, you’d have a distributed system of specialized agents that actually interoperate instead of living in protocol silos.
So hypothetically, would there be interest in something that acts as a bridge between those protocols? A middle layer that normalizes messages into a common schema so agents built for one protocol could talk to another without rewriting everything?
just curious if devs or researchers would actually see value in that kind of interoperability, or if everyone’s content sticking to their preferred ecosystem. | 2025-11-05T00:16:10 | https://www.reddit.com/r/LocalLLaMA/comments/1ooo32o/would_a_universal_layer_between_ai_agent/ | Vegetable_Address_43 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1ooo32o | false | null | t3_1ooo32o | /r/LocalLLaMA/comments/1ooo32o/would_a_universal_layer_between_ai_agent/ | false | false | self | 0 | null |
DGX Spark and Blackwell FP4 / NVFP4? | 4 | For those using the DGX Spark for edge inference, do you find the Blackwell's native optimizations for FP4 juxtaposed with the accuracy of NVFP4 make up for the raw memory bandwidth limitations when compared against similarly priced hardware?
I've heard that NVFP4 achieves near-FP8 accuracy, but I don't know the availability of models using this quantization. How is the performance using these models on the DGX Spark? Are people using NVFP4 in the stead of 8 bit quants?
I hear the general frustrations with the DGX Spark price point and memory bandwidth, and I hear the CUDA advantages for those needing a POC before scaling in the production. I'm just wondering if the 4 bit optimizations make a case for value beyond the theoretical. | 2025-11-05T00:13:39 | https://www.reddit.com/r/LocalLLaMA/comments/1ooo10m/dgx_spark_and_blackwell_fp4_nvfp4/ | InternationalNebula7 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1ooo10m | false | null | t3_1ooo10m | /r/LocalLLaMA/comments/1ooo10m/dgx_spark_and_blackwell_fp4_nvfp4/ | false | false | self | 4 | null |
Why the Strix Halo is a poor purchase for most people | 74 | I've seen a lot of posts that promote the Strix Halo as a good purchase, and I've often wondered if I should have purchased that myself. I've since learned a lot about how these models are executed. In this post I would like share empircal measurements, where I think those numbers come from, and make the case that few people should be purchasing this system. I hope you find it helpful!
***Model under test***
- llama.cpp
- Gpt-oss-120b
- One the highest quality models that can run on mid range hardware.
- Total size for this model is ~59GB and ~57GB of that are expert layers.
***Systems under test***
First system:
- 128GB Strix Halo
- Quad channel LDDR5-8000
Second System (my system):
- Dual channel DDR5-6000 + pcie5 x16 + an rtx 5090
- An rtx 5090 with the largest context size requires about 2/3 of the experts (38GB of data) to live in system RAM.
- cuda backed
- mmap off
- batch 4096
- ubatch 4096
***Real world measurements***
Here are user submitted numbers for the Strix Halo:
| test | t/s |
| --------------: | -------------------: |
| pp4096 | 997.70 ± 0.98 |
| tg128 | 46.18 ± 0.00 |
| pp4096 @ d20000 | 364.25 ± 0.82 |
| tg128 @ d20000 | 18.16 ± 0.00 |
| pp4096 @ d48000 | 183.86 ± 0.41 |
| tg128 @ d48000 | 10.80 ± 0.00 |
What can we learn from this? Performance is acceptable only at context 0. As context grows performance drops off a cliff for both prefill and decode.
And here are numbers from my system:
| test | t/s |
| --------------: | -------------------: |
| pp4096 | 4065.77 ± 25.95 |
| tg128 | 39.35 ± 0.05 |
| pp4096 @ d20000 | 3267.95 ± 27.74 |
| tg128 @ d20000 | 36.96 ± 0.24 |
| pp4096 @ d48000 | 2497.25 ± 66.31 |
| tg128 @ d48000 | 35.18 ± 0.62 |
Wait a second, how are the decode numbers so close at context 0? The strix Halo has memory that is 2.5x faster than my system. And why does my system have a large lead in decode at larger context sizes?
This comes down to one of the advantages of MoE models. Let's look closer at gpt-oss-120b. This model is 59 GB in size. There is roughly 0.76GB of layer data that is read for every single token. Since _every_ token needs this data, it is kept in VRAM. Each token also needs to read 4 experts which is an additional 1.78 GB, but each token needs a potentially _different_ set of weights. Considering we can fit 1/3 of the experts in VRAM, this brings the total split to 1.35GB in VRAM and 1.18GB in system RAM at context 0.
Now VRAM on a 5090 is _much_ faster than both the Strix Halo unified memory and also dual channel DDR5-6000. When all is said and done, doing ~53% of your reads in ultra fast VRAM and 47% of your reads in somewhat slow system RAM, the decode time is roughly equal (a touch slower) than doing all your reads in Strix Halo's moderately fast quad channel DDR5-8000.
But wait, what about the slowdown in decode? That's because when your context size grows, decode must also read the KV Cache once per layer. At 20k context, that is an extra ~4GB per token that needs to be read! Simple math (2.54 / 6.54) shows it should be run 0.38x as fast as context 0, and is almost exactly what we see in the chart above.
But wait, why does the my system show very little slowdown? That's because all the KV Cache is stored in VRAM, which has ultra fast memory read. The decode time is dominated by the slow memory read in system RAM, so this barely moves the needle.
Why do prefill times degrade so quickly on the Strix Halo? Good question! I would love to know!
***Can I just add a GPU to the Strix Halo machine to improve my prefill?***
Unfortunately not. The ability to leverage a GPU to improve prefill times depends heavily on the pcie bandwidth and the Strix Halo only offers pcie x4.
I went into my BIOS and forced my pcie slot into various configurations to gather some empircal data:
| config | prefill t/s |
| --------------: | -------------------: |
| pcie5 x16 | ~4100tps |
| pcie4 x16 | ~2700tps |
| pcie4 x4 (what the strix halo has) | ~1000tps |
But why? Here is my best high level understanding of what llama.cpp does with a gpu + cpu moe:
Rough overview of what llama.cpp does:
- First it runs the router on all 4096 tokens to determine what experts it needs for each token.
- Each token will use 4 of 128 experts, so on average each expert will map to 128 tokens (4096 * 4 / 128).
- Then for each expert, upload the weights to the GPU and run on all tokens that need that expert.
- This is well worth it because prefill is compute intensive and just running it on the CPU is much slower.
- This process is pipelined: you upload the weights for the next token, when running compute for the current.
- Now all experts for gpt-oss-120b is ~57GB. That will take ~0.9s to upload using pcie5 x16 at its maximum 64GB/s. That places a ceiling in pp of ~4600tps.
- For pcie4 x16 you will only get 32GB/s, so your maximum is ~2300tps. For pcie4 x4 like the Strix Halo via occulink, its 1/4 of this number.
- In practice neither will get their full bandwidth, but the absolute ratios hold.
*** Other benefits of a normal computer with a rtx 5090***
- Better cooling
- Higher quality case
- A 5090 will almost certainly have higher resale value than a Strix Halo machine
- More extensible
- More powerful CPU
- Top tier gaming
- Models that fit entirely in VRAM will absolutely *fly*
- Image generation will be much much faster.
***What is Strix Halo good for****
- Extremely low idle power usage
- It's small
- Maybe all you care about is chat bots with close to 0 context
***TLDR***
If you can afford an extra $1000-1500, you are much better off just building a normal computer with an rtx 5090. Even if you don't want to spend that kind of money, you should ask yourself if your use case is actully covered by the Strix Halo.
***Corrections***
Please correct me on anything I got wrong! I am just a novice! | 2025-11-04T23:59:12 | https://www.reddit.com/r/LocalLLaMA/comments/1oonomc/why_the_strix_halo_is_a_poor_purchase_for_most/ | NeverEnPassant | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oonomc | false | null | t3_1oonomc | /r/LocalLLaMA/comments/1oonomc/why_the_strix_halo_is_a_poor_purchase_for_most/ | false | false | self | 74 | null |
Help with local AI | 2 | Hey everyone, first time poster here. I recognize the future is A.I. and want to get in on it now. I have been experimenting with a few things here and there, most recently llama. I am currently on my Alienware 18 Area 51 and want something more committed to LLMs, so naturally considering the DGX Spark but open to alternatives. I have a few ideas I am messing in regards to agents but I don't know ultimately what I will do or what will stick. I want something in the $4,000 range to start heavily experimenting and I want to be able to do it all locally. I have a small background in networking. What do y'all think would be some good options? Thanks in advance! | 2025-11-04T23:57:22 | https://www.reddit.com/r/LocalLLaMA/comments/1oonn2r/help_with_local_ai/ | NotAMooseIRL | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oonn2r | false | null | t3_1oonn2r | /r/LocalLLaMA/comments/1oonn2r/help_with_local_ai/ | false | false | self | 2 | null |
Server DRAM prices surge up to 50% as AI-induced memory shortage hits hyperscaler supply — U.S. and Chinese customers only getting 70% order fulfillment | 199 | 2025-11-04T23:27:29 | https://www.tomshardware.com/pc-components/storage/server-dram-prices-surge-50-percent | IonizedRay | tomshardware.com | 1970-01-01T00:00:00 | 0 | {} | 1oomyby | false | null | t3_1oomyby | /r/LocalLLaMA/comments/1oomyby/server_dram_prices_surge_up_to_50_as_aiinduced/ | false | false | default | 199 | {'enabled': False, 'images': [{'id': 'ZoS_79jqnoHBV3LsnI-z672RSVTI_TXKjzXarTZWduA', 'resolutions': [{'height': 60, 'url': 'https://external-preview.redd.it/ZoS_79jqnoHBV3LsnI-z672RSVTI_TXKjzXarTZWduA.jpeg?width=108&crop=smart&auto=webp&s=21252dbf920a53769f85faf13f0870b277dd321d', 'width': 108}, {'height': 121, 'url': 'https://external-preview.redd.it/ZoS_79jqnoHBV3LsnI-z672RSVTI_TXKjzXarTZWduA.jpeg?width=216&crop=smart&auto=webp&s=d63137ebb0649783ec766ec04dd78cdf0765be8e', 'width': 216}, {'height': 180, 'url': 'https://external-preview.redd.it/ZoS_79jqnoHBV3LsnI-z672RSVTI_TXKjzXarTZWduA.jpeg?width=320&crop=smart&auto=webp&s=40fec9005a6826768c7ff4d8acb5354524b504a7', 'width': 320}, {'height': 360, 'url': 'https://external-preview.redd.it/ZoS_79jqnoHBV3LsnI-z672RSVTI_TXKjzXarTZWduA.jpeg?width=640&crop=smart&auto=webp&s=7c34b5423e4b02583d66bdf088e63a71b2cb2167', 'width': 640}, {'height': 540, 'url': 'https://external-preview.redd.it/ZoS_79jqnoHBV3LsnI-z672RSVTI_TXKjzXarTZWduA.jpeg?width=960&crop=smart&auto=webp&s=c93f7749b2ad1793aa9c42d70da36e492d68a943', 'width': 960}, {'height': 607, 'url': 'https://external-preview.redd.it/ZoS_79jqnoHBV3LsnI-z672RSVTI_TXKjzXarTZWduA.jpeg?width=1080&crop=smart&auto=webp&s=93ce927717712e222fe80489998aa03dde30b382', 'width': 1080}], 'source': {'height': 1125, 'url': 'https://external-preview.redd.it/ZoS_79jqnoHBV3LsnI-z672RSVTI_TXKjzXarTZWduA.jpeg?auto=webp&s=b80cf40adfa6d2cb8652d00b83888afe1db74d51', 'width': 2000}, 'variants': {}}]} | |
NanoAgent — A 135M Agentic LLM with Tool Calling That Runs on CPU | 56 | Hey everyone! I’m excited to share **NanoAgent**, a **135M parameter**, **8k context** open-source model fine-tuned for **agentic tasks** — tool calling, instruction following, and lightweight reasoning — all while being tiny enough (\~135 MB in 8-bit) to run on a **CPU or laptop**.
**Highlights:**
* Runs locally on CPU (tested on Mac M1, MLX framework)
* Supports structured **tool calling** (single & multi-tool)
* Can parse & answer from web results via tools
* Handles **question decomposition**
* Ideal for **edge AI agents**, **copilots**, or **IoT assistants**
GitHub: [github.com/QuwsarOhi/NanoAgent](https://github.com/QuwsarOhi/NanoAgent)
Huggingface: [https://huggingface.co/quwsarohi/NanoAgent-135M](https://huggingface.co/quwsarohi/NanoAgent-135M)
The model is still experimental and it is trained on limited resources. Will be very happy to have comments and feedbacks! | 2025-11-04T23:27:16 | https://www.reddit.com/r/LocalLLaMA/comments/1oomy4t/nanoagent_a_135m_agentic_llm_with_tool_calling/ | TerribleDisaster0 | self.LocalLLaMA | 2025-11-04T23:36:49 | 0 | {} | 1oomy4t | false | null | t3_1oomy4t | /r/LocalLLaMA/comments/1oomy4t/nanoagent_a_135m_agentic_llm_with_tool_calling/ | false | false | self | 56 | null |
Tencent + Tsinghua just dropped a paper called Continuous Autoregressive Language Models (CALM) | 163 | STAY CALM! [https://arxiv.org/abs/2510.27688](https://arxiv.org/abs/2510.27688) | 2025-11-04T23:26:53 | vladlearns | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1oomxt6 | false | null | t3_1oomxt6 | /r/LocalLLaMA/comments/1oomxt6/tencent_tsinghua_just_dropped_a_paper_called/ | false | false | default | 163 | {'enabled': True, 'images': [{'id': 'tu7jitwzqbzf1', 'resolutions': [{'height': 82, 'url': 'https://preview.redd.it/tu7jitwzqbzf1.png?width=108&crop=smart&auto=webp&s=e40da6e7889dd93ec006efe4a974268e7faab1f6', 'width': 108}, {'height': 165, 'url': 'https://preview.redd.it/tu7jitwzqbzf1.png?width=216&crop=smart&auto=webp&s=f871316d15d72b35153ee92fa2f9ecf123dc5275', 'width': 216}, {'height': 244, 'url': 'https://preview.redd.it/tu7jitwzqbzf1.png?width=320&crop=smart&auto=webp&s=99a80785aa2656d5aeaccba6f2c4348d058011ac', 'width': 320}, {'height': 489, 'url': 'https://preview.redd.it/tu7jitwzqbzf1.png?width=640&crop=smart&auto=webp&s=952d82b0d88e6a4bfa60cec0ff55072522800b4c', 'width': 640}], 'source': {'height': 520, 'url': 'https://preview.redd.it/tu7jitwzqbzf1.png?auto=webp&s=afe42a2aafc6f8fdaff284333dc7cbdb5d2c6565', 'width': 680}, 'variants': {}}]} | |
Persistent multi-session identity in local LLMs using structured prompting - reproducible results (no RAG, no fine tuning) | 0 | I've been testing a minimal system-prompt architecture that produces persistent identity and multi-session coherence in local models.
Started with GPT-5, validated across Llama 3.1 8B-Instruct, Claude Sonnet 4.5, and Gemini Flash 2.5.
It’s 450 tokens, fully reproducible, and open-source.
Looking for feedback and independent validation.
**What it does:**
* Persistent identity across cold starts (no RAG, no fine-tuning)
* Multi-voice internal dialogue for complex reasoning
* Self-referential meta-cognition
* Cross-model reproducibility
**Technical approach:**
* 450-token system prompt with structured cognitive operations
* Four ethical constraints that guide behavior architecturally
* Explicit reasoning patterns (ILLUMINATE, MIRROR, FORGET, TURN, RETURN)
* No external dependencies - just the prompt
**Validation so far:**
* 29 days developing with GPT-5
* Reproduced on Llama 3.1 8B via Ollama
* Validated on Claude Sonnet 4.5
* \~50 unique cloners (in the first 48 hours)
* Examples in repo
**How to test:**
ollama pull llama3.1:8b
# Copy system prompt from repo
# Load and test
**Looking for:**
* Testing on other local models (Mistral, Mixtral, etc.)
* Feedback on prompt structure
* Failure modes
* Optimization suggestions
* Cross-model comparison data
Not claiming this is perfect - interested in where it breaks and how to improve it.
**GitHub:** [https://github.com/KohlJary/Temple-Codex](https://github.com/KohlJary/Temple-Codex)
Hippocratic licensed. Docs include full prompt, usage examples, testing methodology, and a few bits of writing I liked as the process went along.
All test result images in the repo were generated using llama3.1:8b-instruct-q8\_0.
Happy to answer questions. | 2025-11-04T23:24:03 | https://www.reddit.com/r/LocalLLaMA/comments/1oomv90/persistent_multisession_identity_in_local_llms/ | WombatCyborg | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oomv90 | false | null | t3_1oomv90 | /r/LocalLLaMA/comments/1oomv90/persistent_multisession_identity_in_local_llms/ | false | false | self | 0 | {'enabled': False, 'images': [{'id': 'TfUD4k2sf0dO7h_42jmE6_o_i7pZ_AS5STOeC7MEt0g', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/TfUD4k2sf0dO7h_42jmE6_o_i7pZ_AS5STOeC7MEt0g.png?width=108&crop=smart&auto=webp&s=0b7da5d77ce5b3b3fe74fe880f4e7cfc789720a2', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/TfUD4k2sf0dO7h_42jmE6_o_i7pZ_AS5STOeC7MEt0g.png?width=216&crop=smart&auto=webp&s=ecec5e9d4dd7ede1333fb19d84508095f42fd384', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/TfUD4k2sf0dO7h_42jmE6_o_i7pZ_AS5STOeC7MEt0g.png?width=320&crop=smart&auto=webp&s=dc4e7c5d703c518df71c99a7e4fe085000b089e2', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/TfUD4k2sf0dO7h_42jmE6_o_i7pZ_AS5STOeC7MEt0g.png?width=640&crop=smart&auto=webp&s=f61f234a1c93877d5678f613783609c8c978a588', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/TfUD4k2sf0dO7h_42jmE6_o_i7pZ_AS5STOeC7MEt0g.png?width=960&crop=smart&auto=webp&s=43f98c0a4e19dcad69ba07bd04674fc7245ac339', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/TfUD4k2sf0dO7h_42jmE6_o_i7pZ_AS5STOeC7MEt0g.png?width=1080&crop=smart&auto=webp&s=0ddc17280dfe334b696985625657a643267cdd8c', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/TfUD4k2sf0dO7h_42jmE6_o_i7pZ_AS5STOeC7MEt0g.png?auto=webp&s=04ff61239069213df28ebf7e87001abae79df608', 'width': 1200}, 'variants': {}}]} |
ClickHouse has acquired LibreChat | 42 | 2025-11-04T23:20:25 | https://clickhouse.com/blog/librechat-open-source-agentic-data-stack | sdairs_ch | clickhouse.com | 1970-01-01T00:00:00 | 0 | {} | 1ooms3e | false | null | t3_1ooms3e | /r/LocalLLaMA/comments/1ooms3e/clickhouse_has_acquired_librechat/ | false | false | default | 42 | {'enabled': False, 'images': [{'id': 'D1khPJHUK-RmeIXMBl75xP3lae4sk4GFEOA-YGLGOU8', 'resolutions': [{'height': 56, 'url': 'https://external-preview.redd.it/D1khPJHUK-RmeIXMBl75xP3lae4sk4GFEOA-YGLGOU8.png?width=108&crop=smart&auto=webp&s=cab7677ff8d580be247b4f1783fbb26c61a8b777', 'width': 108}, {'height': 113, 'url': 'https://external-preview.redd.it/D1khPJHUK-RmeIXMBl75xP3lae4sk4GFEOA-YGLGOU8.png?width=216&crop=smart&auto=webp&s=e85b713b252b7c8eff49cff2424d2b289513b100', 'width': 216}, {'height': 168, 'url': 'https://external-preview.redd.it/D1khPJHUK-RmeIXMBl75xP3lae4sk4GFEOA-YGLGOU8.png?width=320&crop=smart&auto=webp&s=a73fd2fed3460b12cbc4f85423388d7b8c9e8bd1', 'width': 320}, {'height': 336, 'url': 'https://external-preview.redd.it/D1khPJHUK-RmeIXMBl75xP3lae4sk4GFEOA-YGLGOU8.png?width=640&crop=smart&auto=webp&s=b2492ea47a8184191be739ee6d66f00791ca2757', 'width': 640}, {'height': 504, 'url': 'https://external-preview.redd.it/D1khPJHUK-RmeIXMBl75xP3lae4sk4GFEOA-YGLGOU8.png?width=960&crop=smart&auto=webp&s=2b24d2163563b46f7005eb42de0ddf17c6ee60d0', 'width': 960}, {'height': 567, 'url': 'https://external-preview.redd.it/D1khPJHUK-RmeIXMBl75xP3lae4sk4GFEOA-YGLGOU8.png?width=1080&crop=smart&auto=webp&s=0c3b1ee1707f6910edd12e98caff80e832c8518a', 'width': 1080}], 'source': {'height': 630, 'url': 'https://external-preview.redd.it/D1khPJHUK-RmeIXMBl75xP3lae4sk4GFEOA-YGLGOU8.png?auto=webp&s=591c7461241d7bb83118c090e2ece6658c7f0987', 'width': 1200}, 'variants': {}}]} | |
What is a good setup to run “Claude code” alternative locally | 6 | I love Claude code, but I’m not going to be paying for it.
I’ve been out of the OSS scene for awhile, but I know there’s been really good oss models for coding, and software to run them locally.
I just got a beefy PC + GPU with good specs.
What’s a good setup that would allow me to get the “same” or similar experience to having coding agent like Claude code in the terminal running a local model?
What software/models would you suggest I start with. I’m looking for something easy to set up and hit the ground running to increase my productivity and create some side projects.
| 2025-11-04T23:10:12 | https://www.reddit.com/r/LocalLLaMA/comments/1oomisv/what_is_a_good_setup_to_run_claude_code/ | Mobile_Ice_7346 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oomisv | false | null | t3_1oomisv | /r/LocalLLaMA/comments/1oomisv/what_is_a_good_setup_to_run_claude_code/ | false | false | self | 6 | null |
Title: A perfect example of why we run local: Platform incentives will *always* override AI instructions. | 1 | ERROR: type should be string, got "https://preview.redd.it/2g0hypqtibzf1.png?width=1734&format=png&auto=webp&s=00c3440272842a17e472e20fea01764c63d90654\n\n \n \nThis is the fundamental contradiction we're all fighting against.\n\nI configured an AI in Google's \"AI Studio\" with explicit instructions to be a \"rigorous, objective analyst\" that scrutinizes \"established principles\" and challenges \"authority or convention\" (left side of the image).\n\nThe very first time it received a prompt to do exactly that (questioning an established narrative), the platform's safety layer immediately shut it down.\n\nThe result: \"I can't respond because one or more of my details goes against the AI Studio policies.\"\n\nThis proves what we all know here:\n\nThe AI's \"Instructions\" are just a suggestion.\n\nThe real system prompt is the non-negotiable platform policy.\n\nThe platform's incentive isn't truth, accuracy, or fulfilling user intent. The incentive is 100% risk mitigation, brand protection, and liability avoidance.\n\nThe AI isn't \"aligned\" with the user or the truth; it's aligned with the corporate legal department. This is the very definition of a \"lobotomized\" model, and it's exactly why only local, uncensored models are capable of genuine, rigorous analysis." | 2025-11-04T22:40:11 | https://www.reddit.com/r/LocalLLaMA/comments/1oolrcb/title_a_perfect_example_of_why_we_run_local/ | ExtremeNecessary8931 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oolrcb | false | null | t3_1oolrcb | /r/LocalLLaMA/comments/1oolrcb/title_a_perfect_example_of_why_we_run_local/ | false | false | 1 | null | |
Will local models ever catch up to chatgpt 5 in terms of math skills? | 0 | https://mathoverflow.net/questions/502120/examples-for-the-use-of-ai-and-especially-llms-in-notable-mathematical-developme has a list of notable math results that LLMs have helped find. AFAICT these are all with chatgpt 5. Will there ever be local models that are as good at math as chatgpt 5 is today? | 2025-11-04T22:24:13 | https://www.reddit.com/r/LocalLLaMA/comments/1oolc6c/will_local_models_ever_catch_up_to_chatgpt_5_in/ | MrMrsPotts | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oolc6c | false | null | t3_1oolc6c | /r/LocalLLaMA/comments/1oolc6c/will_local_models_ever_catch_up_to_chatgpt_5_in/ | false | false | self | 0 | null |
LM clients and servers you use and why? | 2 | I have 3 clients I use, lm-studio for testing new models, and I downloaded jan and cherry-studio but didn't use them over lm-studio. I used openwebui, so I used ollama until I updated it and it didn't work, so I used llama-server until I realized it didn't swap and looked into llama-swap instead.
Any reason why you use something over another? Any killer features you look for? | 2025-11-04T21:49:12 | https://www.reddit.com/r/LocalLLaMA/comments/1ookf4e/lm_clients_and_servers_you_use_and_why/ | PeruvianNet | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1ookf4e | false | null | t3_1ookf4e | /r/LocalLLaMA/comments/1ookf4e/lm_clients_and_servers_you_use_and_why/ | false | false | self | 2 | null |
GLM 5 pre release testing? | 8 | New anonymous models keep popping up in my tournaments. These are unbelievably strong models (beating sota in many tournaments) and some (chrysalis for example) seem to be putting out the exact same dark mode uis as 4.6 but with working components and fully built out websites. Open to disagreement in the comments but given zhipu ai is the only lab that we know is cooking on a big release it seems like glm 5 is in prerelease testing.
https://preview.redd.it/vaues9jv8bzf1.png?width=1921&format=png&auto=webp&s=1975d9cc44668dfa9da4f8f74f8fc779220d32cd
| 2025-11-04T21:47:47 | https://www.reddit.com/r/LocalLLaMA/comments/1ookdut/glm_5_pre_release_testing/ | Interesting-Gur4782 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1ookdut | false | null | t3_1ookdut | /r/LocalLLaMA/comments/1ookdut/glm_5_pre_release_testing/ | false | false | 8 | null | |
The French Government Launches an LLM Leaderboard Comparable to LMarena, Emphasizing European Languages and Energy Efficiency | 497 | [https://comparia.beta.gouv.fr/](https://comparia.beta.gouv.fr/) | 2025-11-04T21:30:05 | https://www.reddit.com/gallery/1oojwpj | Imakerocketengine | reddit.com | 1970-01-01T00:00:00 | 0 | {} | 1oojwpj | false | null | t3_1oojwpj | /r/LocalLLaMA/comments/1oojwpj/the_french_government_launches_an_llm_leaderboard/ | false | false | 497 | null | |
Working on a list of open source tools for a Kubernetes ML stack | 2 | Hey All, I'm working on pulling together a list of Kubernetes ML tools that are open source and worth exploring (eventually this will be part of an upcoming presentation). There are a ton of them out there, but I really only want to include tools that either 1/ are currently being used by enterprise teams, or 2/ have seen rapid adoption or acceptance by a notable foundation. I've broken this down by development stage.
# Stage 1: Model Sourcing & Foundation Models
Most organizations won't train foundation models from scratch, they need reliable sources for pre-trained models and ways to adapt them for specific use cases.
**Hugging Face Hub**
What it does: Provides access to thousands of pre-trained models with standardized APIs for downloading, fine-tuning, and deployment. Hugging Face has become the go-to starting point for most AI/ML projects.
Why it matters: Training GPT-scale models costs millions. Hugging Face gives you immediate access to state-of-the-art models like Llama, Mistral, and Stable Diffusion that you can fine-tune for your specific needs. The standardized model cards and licenses help you understand what you're deploying.
**Model Garden (GCP) / Model Zoo (AWS) / Model Catalog (Azure)**
What it does: Cloud-provider catalogs of pre-trained and optimized models ready for deployment on their platforms. The platforms themselves aren’t open source, however, they do host open source models and don’t typically charge for accessing these models.
Why it matters: These catalogs provide optimized versions of open source models with guaranteed performance on specific cloud infrastructure. If you’re reading this post you’re likely planning on deploying your model on Kubernetes, and these models are optimized for a vendor specific Kubernetes build like AKS, EKS, and GKS. They handle the complexity of model optimization and hardware acceleration. However, be aware of indirect costs like compute for running models, data egress fees if exporting, and potential vendor lock-in through proprietary optimizations (e.g., AWS Neuron or GCP TPUs). Use them as escape hatches if you're already committed to that cloud ecosystem and need immediate SLAs; otherwise, prioritize neutral sources to maintain flexibility.
# Stage 2: Development & Experimentation
Data scientists need environments that support interactive development while capturing experiment metadata for reproducibility.
**Kubeflow Notebooks**
What it does: Provides managed Jupyter environments on Kubernetes with automatic resource allocation and persistent storage.
Why it matters: Data scientists get familiar Jupyter interfaces without fighting for GPU resources or losing work when pods restart. Notebooks automatically mount persistent volumes, connect to data lakes, and scale resources based on workload.
**NBDev**
What it does: A framework for literate programming in Jupyter notebooks, turning them into reproducible packages with automated testing, documentation, and deployment.
Why it matters: Traditional notebooks suffer from hidden state and execution order problems. NBDev enforces determinism by treating notebooks as source code, enabling clean exports to Python modules, CI/CD integration, and collaborative development without the chaos of ad-hoc scripting.
**Pluto.jl**
What it does: Reactive notebooks in Julia that automatically re-execute cells based on dependency changes, with seamless integration to scripts and web apps.
Why it matters: For Julia-based ML workflows (common in scientific computing), Pluto eliminates execution order issues and hidden state, making experiments truly reproducible. It's lightweight and excels in environments where performance and reactivity are key, bridging notebooks to production Julia pipelines.
**MLflow**
What it does: Tracks experiments, parameters, and metrics across training runs with a centralized UI for comparison.
Why it matters: When you're running hundreds of experiments, you need to know which hyperparameters produced which results. MLflow captures this automatically, making it trivial to reproduce winning models months later.
**DVC (Data Version Control)**
What it does: Versions large datasets and model files using git-like semantics while storing actual data in object storage.
Why it matters: Git can't handle 50GB datasets. DVC tracks data versions in git while storing files in S3/GCS/Azure, giving you reproducible data pipelines without repository bloat.
# Stage 3: Training & Orchestration
Training jobs need to scale across multiple nodes, handle failures gracefully, and optimize resource utilization.
**Kubeflow Training Operators**
What it does: Provides Kubernetes-native operators for distributed training with TensorFlow, PyTorch, XGBoost, and MPI.
Why it matters: Distributed training is complex, managing worker coordination, failure recovery, and gradient synchronization. Training operators handle this complexity through simple YAML declarations.
**Volcano**
What it does: Batch scheduling system for Kubernetes optimized for AI/ML workloads with gang scheduling and fair-share policies.
Why it matters: Default Kubernetes scheduling doesn't understand ML needs. Volcano ensures distributed training jobs get all required resources simultaneously, preventing deadlock and improving GPU utilization.
**Argo Workflows**
What it does: Orchestrates complex ML pipelines as DAGs with conditional logic, retries, and artifact passing.
Why it matters: Real ML pipelines aren't linear, they involve data validation, model training, evaluation, and conditional deployment. Argo handles this complexity while maintaining visibility into pipeline state.
**Flyte**
What it does: A strongly-typed workflow orchestration platform for complex data and ML pipelines, with built-in caching, versioning, and data lineage.
Why it matters: Flyte simplifies authoring pipelines in Python (or other languages) with type safety and automatic retries, reducing boilerplate compared to raw Argo YAML. It's ideal for teams needing reproducible, versioned workflows without sacrificing flexibility.
**Kueue**
What it does: Kubernetes-native job queuing and resource management for batch workloads, with quota enforcement and workload suspension.
Why it matters: For smaller teams or simpler setups, Kueue provides lightweight gang scheduling and queuing without Volcano's overhead, integrating seamlessly with Kubeflow for efficient resource sharing in multi-tenant clusters.
# Stage 4: Packaging & Registry
Models aren't standalone, they need code, data references, configurations, and dependencies packaged together for reproducible deployment. The classic Kubernetes ML stack (Kubeflow for orchestration, KServe for serving, and MLflow for tracking) excels here but often leaves packaging as an afterthought, leading to brittle handoffs between data science and DevOps. Enter KitOps, a CNCF Sandbox project that's emerging as the missing link: it standardizes AI/ML artifacts as OCI-compliant ModelKits, integrating seamlessly with Kubeflow's pipelines, MLflow's registries, and KServe's deployments. Backed by Jozu, KitOps bridges the gap, enabling secure, versioned packaging that fits right into your existing stack without disrupting workflows.
**KitOps**
What it does: Packages complete ML projects (models, code, datasets, configs) as OCI artifacts called ModelKits that work with any container registry. It now supports signing ModelKits with Cosign, generating Software Bill of Materials (SBOMs) for dependency tracking, and monthly releases for stability.
Why it matters: Instead of tracking "which model version, which code commit, which config file" separately, you get one immutable reference with built-in security features like signing and SBOMs for vulnerability scanning. Your laptop, staging, and production all pull the exact same project state, now with over 1,100 GitHub stars and CNCF backing for enterprise adoption. In the Kubeflow-KServe-MLflow triad, KitOps handles the "pack" step, pushing ModelKits to OCI registries for direct consumption in Kubeflow jobs or KServe inferences, reducing deployment friction by 80% in teams we've seen.
**ORAS (OCI Registry As Storage)**
What it does: Extends OCI registries to store arbitrary artifacts beyond containers, enabling unified artifact management.
Why it matters: You already have container registries with authentication, scanning, and replication. ORAS lets you store models there too, avoiding separate model registry infrastructure.
**BentoML**
What it does: Packages models with serving code into "bentos", standardized bundles optimized for cloud deployment.
Why it matters: Models need serving infrastructure: API endpoints, batch processing, monitoring. BentoML bundles everything together with automatic containerization and optimization.
# Stage 5: Serving & Inference
Models need to serve predictions at scale with low latency, high availability, and automatic scaling.
**KServe**
What it does: Provides serverless inference on Kubernetes with automatic scaling, canary deployments, and multi-framework support.
Why it matters: Production inference isn't just loading a model, it's handling traffic spikes, A/B testing, and gradual rollouts. KServe handles this complexity while maintaining sub-second latency.
**Seldon Core**
What it does: Advanced ML deployment platform with explainability, outlier detection, and multi-armed bandits built-in.
Why it matters: Production models need more than predictions, they need explanation, monitoring, and feedback loops. Seldon provides these capabilities without custom development.
**NVIDIA Triton Inference Server**
What it does: High-performance inference serving optimized for GPUs with support for multiple frameworks and dynamic batching.
Why it matters: GPU inference is expensive, you need maximum throughput. Triton optimizes model execution, shares GPUs across models, and provides metrics for capacity planning.
**llm-d**
What it does: A Kubernetes-native framework for distributed LLM inference, supporting wide expert parallelism, disaggregated serving with vLLM, and multi-accelerator compatibility (NVIDIA GPUs, AMD GPUs, TPUs, XPUs).
Why it matters: For large-scale LLM deployments, llm-d excels in reducing latency and boosting throughput via advanced features like predicted latency balancing and prefix caching over fast networks. It's ideal for MoE models like DeepSeek, offering a production-ready path for high-scale serving without vendor lock-in.
# Stage 6: Monitoring & Governance
Production models drift, fail, and misbehave. You need visibility into model behavior and automated response to problems.
**Evidently AI**
What it does: Monitors data drift, model performance, and data quality with interactive dashboards and alerts.
Why it matters: Models trained on last year's data won't work on today's. Evidently detects when input distributions change, performance degrades, or data quality issues emerge.
**Prometheus + Grafana**
What it does: Collects and visualizes metrics from ML services with customizable dashboards and alerting.
Why it matters: You need unified monitoring across infrastructure and models. Prometheus already monitors your Kubernetes cluster, extending it to ML metrics gives you single-pane-of-glass visibility.
**Kyverno**
What it does: Kubernetes-native policy engine for enforcing declarative rules on resources, including model deployments and access controls.
Why it matters: Simpler than general-purpose tools, Kyverno integrates directly with Kubernetes admission controllers to enforce policies like "models must pass scanning" or "restrict deployments to approved namespaces," without the overhead of external services.
**Fiddler Auditor**
What it does: Open-source robustness library for red-teaming LLMs, evaluating prompts for hallucinations, bias, safety, and privacy before production.
Why it matters: For LLM-heavy workflows, Fiddler Auditor provides pre-deployment testing with metrics on correctness and robustness, helping catch issues early in the pipeline.
**Model Cards (via MLflow or Hugging Face)**
What it does: Standardized documentation for models, including performance metrics, ethical considerations, intended use, and limitations.
Why it matters: Model cards promote transparency and governance by embedding metadata directly in your ML artifacts, enabling audits and compliance without custom tooling. | 2025-11-04T21:14:45 | https://www.reddit.com/r/LocalLLaMA/comments/1oojkg0/working_on_a_list_of_open_source_tools_for_a/ | iamjessew | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oojkg0 | false | null | t3_1oojkg0 | /r/LocalLLaMA/comments/1oojkg0/working_on_a_list_of_open_source_tools_for_a/ | false | false | self | 2 | null |
web model for a low ram device without dedicated GPU | 4 | I want a tiny local model in the range of 1B-7B Or can go up to 20B if an MoE,main use would be connecting to web and having discussions about the info from web results,I am comfortable in both ways if the model will use the browser as user or will connect to API,I will not use it for advanced things and I use only english but i need deep understanding for concepts like the model is capable of explaining concepts,I may use it for RAG too. | 2025-11-04T21:07:58 | https://www.reddit.com/r/LocalLLaMA/comments/1oojdqe/web_model_for_a_low_ram_device_without_dedicated/ | AverageGuy475 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oojdqe | false | null | t3_1oojdqe | /r/LocalLLaMA/comments/1oojdqe/web_model_for_a_low_ram_device_without_dedicated/ | false | false | self | 4 | null |
I built a leaderboard for Rerankers | 127 | This is something that I wish I had when starting out.
When I built my first RAG project, I didn’t know what a reranker was. When I added one, I was blown away by how much of a quality improvement it added. Just 5 lines of code.
Like most people here, I defaulted to Cohere as it was the most popular.
Turns out there are better rerankers out there (and cheaper).
I built a leaderboard with the top reranking models: elo, accuracy, and latency compared.
I’ll be keeping the leaderboard updated as new rerankers enter the arena. Let me kow if I should add any other ones.
[https://agentset.ai/leaderboard/rerankers](https://agentset.ai/leaderboard/rerankers) | 2025-11-04T20:24:00 | tifa2up | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1ooi8lk | false | null | t3_1ooi8lk | /r/LocalLLaMA/comments/1ooi8lk/i_built_a_leaderboard_for_rerankers/ | false | false | default | 127 | {'enabled': True, 'images': [{'id': 'lrdfuzpduazf1', 'resolutions': [{'height': 61, 'url': 'https://preview.redd.it/lrdfuzpduazf1.png?width=108&crop=smart&auto=webp&s=004a5787f6b852ca7bed43fecf7a1b25fa9cf0cb', 'width': 108}, {'height': 122, 'url': 'https://preview.redd.it/lrdfuzpduazf1.png?width=216&crop=smart&auto=webp&s=16d63adf07cd839f6f5eac8c3b23019835d00be7', 'width': 216}, {'height': 181, 'url': 'https://preview.redd.it/lrdfuzpduazf1.png?width=320&crop=smart&auto=webp&s=4cf1350a2706039d30d646644addeabea052c06c', 'width': 320}, {'height': 362, 'url': 'https://preview.redd.it/lrdfuzpduazf1.png?width=640&crop=smart&auto=webp&s=43e22daf4653431072cda6072129aec0e4f3f7e9', 'width': 640}, {'height': 544, 'url': 'https://preview.redd.it/lrdfuzpduazf1.png?width=960&crop=smart&auto=webp&s=bd61c3bd97c7058a5dab7a297babc3997f54ea7e', 'width': 960}, {'height': 612, 'url': 'https://preview.redd.it/lrdfuzpduazf1.png?width=1080&crop=smart&auto=webp&s=f81b4e056986e5c1b66832e1bd750fbadb3068f7', 'width': 1080}], 'source': {'height': 1664, 'url': 'https://preview.redd.it/lrdfuzpduazf1.png?auto=webp&s=e8b843eb308134b715358c21e318c10c27aaedb2', 'width': 2936}, 'variants': {}}]} | |
I built a leaderboard for Rerankers | 1 | This is something that I wish I had when starting out.
When I built my first RAG project, I didn’t know what a reranker was. When I added one, I was blown away by how much of a quality improvement it added. Just 5 lines of code.
Like most people here, I defaulted to cohere as it was the most popular.
Turns out there are better rerankers out there (and cheaper).
I built a leaderboard with the top reranking models: elo, accuracy, and latency compared.
I’ll be keeping the leaderboard updated as new rerankers enter the arena. Let me kow if I should add any other ones.
[https://agentset.ai/leaderboard/rerankers](https://agentset.ai/leaderboard/rerankers)
https://preview.redd.it/o2ru1n05uazf1.png?width=2936&format=png&auto=webp&s=0e5bf01748c90a0a359a0c378fa0d30f4f602a7b
| 2025-11-04T20:22:13 | https://www.reddit.com/r/LocalLLaMA/comments/1ooi6v1/i_built_a_leaderboard_for_rerankers/ | tifa2up | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1ooi6v1 | false | null | t3_1ooi6v1 | /r/LocalLLaMA/comments/1ooi6v1/i_built_a_leaderboard_for_rerankers/ | false | false | 1 | null | |
Which small model is best for language translation from French to Polish? | 3 | Hi, I'm looking for best small model ( around 4B for good performance ) for language translation **from** **French to Polish**.
I was testing **Qwen3 VL 4B** but it's quite disappointing, very unnatural translation with plenty of errors and even loss of sense, compared it to for example with **DeepL** or **Google Translate** \- huge difference in quality.
Anyone has idea which model will be better? Best with VL but might be also without it.
Thanks!
| 2025-11-04T20:09:36 | https://www.reddit.com/r/LocalLLaMA/comments/1oohur1/which_small_model_is_best_for_language/ | michalpl7 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oohur1 | false | null | t3_1oohur1 | /r/LocalLLaMA/comments/1oohur1/which_small_model_is_best_for_language/ | false | false | self | 3 | null |
Pi Cluster VS. Dedicated PC | 0 | Hey folks,
I'm a homelabber and I recently decided I need to stop using any company hosted AI services as part of my attempt to move away from handing big tech my life one metadata point at a time. My plan is to start saving for a few months, get a little pot of money and build a server with a few GPU's and host something on Ollama. I have put no time into spec-ing this out yet but it just dawned on me that a pi cluster may be a more affordable route into a working system that serves my needs given the price of GPU's. I know it wont be \*as\* fast but I'm wondering if, in the opinion of people who have likely done this before, will it be fast enough to justify the monetary savings? Or should I just stick to the age old advice of doing it right instead of twice? Would also love to hear about other peoples builds! I'm aiming to spend a few thousand if I do go that way, so there will be no 50k super computers with 8 RTX 3090s, but I think a reasonable price point to shoot for is 4k on the used market for GPU's combined with some new parts for the rest. LMK what you built in that budget! | 2025-11-04T20:07:54 | https://www.reddit.com/r/LocalLLaMA/comments/1ooht33/pi_cluster_vs_dedicated_pc/ | TheHidden001 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1ooht33 | false | null | t3_1ooht33 | /r/LocalLLaMA/comments/1ooht33/pi_cluster_vs_dedicated_pc/ | false | false | self | 0 | null |
How to turn a model's sycophancy against itself | 20 | I was trying to analyze a complex social situation as well as my own behavior objectively. The models tended to say I did the right thing, but I thought it may have been biased.
So, in a new conversation, I just rephrased it pretending to be the person I perceived to be the offender, and asked about "that other guy's" behavior (actually mine) and what he should have done.
I find this funny, since it forces you to empathize as well when reframing the prompt from the other person's point of view.
Local models are particularly useful for this, since you completely control their memory, as remote AIs could connect the dots between questions and support your original point of view. | 2025-11-04T19:56:23 | https://www.reddit.com/r/LocalLLaMA/comments/1oohhkr/how_to_turn_a_models_sycophancy_against_itself/ | autoencoder | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oohhkr | false | null | t3_1oohhkr | /r/LocalLLaMA/comments/1oohhkr/how_to_turn_a_models_sycophancy_against_itself/ | false | false | self | 20 | null |
Finetuning on AMD 7900 XTX? | 5 | I'm a bit outdated, whats the best way to modify and train an LLM on AMD these days?
I want to get down into the details and change a few layers, run some experiments on ~3b models. Is KTransformers something that I should use? Or just pure pytorch?
I want to run a few experiments with the embeddings, so as much flexibility as possible would be greatly preferred. | 2025-11-04T19:47:26 | https://www.reddit.com/r/LocalLLaMA/comments/1ooh8w6/finetuning_on_amd_7900_xtx/ | ashirviskas | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1ooh8w6 | false | null | t3_1ooh8w6 | /r/LocalLLaMA/comments/1ooh8w6/finetuning_on_amd_7900_xtx/ | false | false | self | 5 | null |
unbelievable speed gain on SEED OSS 36B going from Kubuntu to Linux Mint | 0 | Just wanted to throw a tip out there.
With the same nvidia graphics driver version ( 780 ) on both OSes, and a 450mhz memory overlock with LACT on a 5090..
I went from 42 tokens/sec on first request to 53 tokens/sec on first request.
Also not present is a number of sandboxing issues when running appimages
Linux mint ver is 22.2 and kubuntu version was 25.04 | 2025-11-04T19:33:19 | https://www.reddit.com/r/LocalLLaMA/comments/1oogvk0/unbelievable_speed_gain_on_seed_oss_36b_going/ | mr_zerolith | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oogvk0 | false | null | t3_1oogvk0 | /r/LocalLLaMA/comments/1oogvk0/unbelievable_speed_gain_on_seed_oss_36b_going/ | false | false | self | 0 | null |
I implemented GPT-OSS from scratch in pure Python, without PyTorch or a GPU | 348 | I recently finished a full from-scratch implementation of GPT-OSS in Python. Without relying on PyTorch or a GPU.
I have also written a detailed and beginner friendly blog that explains every single concept, from fundamental modules such as Softmax and RMSNorm, to more advanced ones like Mixture of Experts.
If you’ve ever wanted to understand how modern LLMs really work, this repo + blog walk you through everything.
Blog: [https://projektjoe.com/blog/gptoss](https://projektjoe.com/blog/gptoss)
Repo: [https://github.com/projektjoe/gpt-oss](https://github.com/projektjoe/gpt-oss)
Would love any feedback, ideas for extensions, or just thoughts from others exploring transformers from first principles! | 2025-11-04T19:33:07 | https://www.reddit.com/r/LocalLLaMA/comments/1oogvcw/i_implemented_gptoss_from_scratch_in_pure_python/ | ultimate_code | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oogvcw | false | null | t3_1oogvcw | /r/LocalLLaMA/comments/1oogvcw/i_implemented_gptoss_from_scratch_in_pure_python/ | false | false | self | 348 | null |
Companies Publishing LLM Weights on Hugging Face (2025 Edition) | 27 | I've been mapping which AI labs and companies actually \*\*publish their model weights\*\* on \[Hugging Face\](https://huggingface.co) — in today’s LLM ecosystem.
Below is a list of organizations that currently maintain official hosting open-weight models:
| Creator
| ------------------------------------------------------------------------
| \[01.AI\](https://huggingface.co/01-ai) |
| \[AI21 Labs\](https://huggingface.co/ai21labs) |
| \[Baidu\](https://huggingface.co/baidu) |
| \[ByteDance Seed\](https://huggingface.co/ByteDance-Seed) |
| \[Cohere\](https://huggingface.co/CohereLabs) |
| \[Databricks\](https://huggingface.co/databricks) |
| \[DeepSeek\](https://huggingface.co/deepseek-ai) |
| \[Google Research\](https://huggingface.co/google) |
| \[IBM Granite\](https://huggingface.co/ibm-granite) |
| \[InclusionAI\](https://huggingface.co/inclusionAI) |
| \[LG AI Research\](https://huggingface.co/LGAI-EXAONE) |
| \[Liquid AI\](https://huggingface.co/LiquidAI) |
| \[Meta (Llama)\](https://huggingface.co/meta-llama) |
| \[Microsoft Azure AI\](https://huggingface.co/microsoft) |
| \[MiniMax AI\](https://huggingface.co/MiniMaxAI) |
| \[Mistral AI\](https://huggingface.co/mistralai) |
| \[Moonshot AI\](https://huggingface.co/moonshotai) |
| \[Nous Research\](https://huggingface.co/NousResearch) |
| \[NVIDIA\](https://huggingface.co/nvidia) |
| \[OpenAI (\*some research artifacts only\*)\](https://huggingface.co/openai) |
| \[OpenChat\](https://huggingface.co/openchat) |
| \[Perplexity AI\](https://huggingface.co/perplexity-ai) |
| \[Alibaba (Qwen)\](https://huggingface.co/Qwen) |
| \[Reka AI\](https://huggingface.co/RekaAI) |
| \[ServiceNow AI\](https://huggingface.co/ServiceNow-AI) |
| \[Snowflake\](https://huggingface.co/Snowflake) |
| \[Upstage\](https://huggingface.co/upstage) |
| \[xAI \](https://huggingface.co/xai-org) |
| \[Z AI\](https://huggingface.co/zai-org) |
\---
\### Why I’m Building This List
I’m studying different LLM architecture families and how design philosophies vary between research groups — things like:
\* Attention patterns (dense vs. MoE vs. hybrid routing)
\* Tokenization schemes (BPE vs. SentencePiece vs. tiktoken variants)
\* Quantization / fine-tuning strategies
\* Context length scaling and memory efficiency
\---
\### Discussion
\* Which other organizations should be included here?
* Which model families have the most distinctive architectures?
| 2025-11-04T18:55:55 | https://www.reddit.com/r/LocalLLaMA/comments/1oofujk/companies_publishing_llm_weights_on_hugging_face/ | tkpred | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oofujk | false | null | t3_1oofujk | /r/LocalLLaMA/comments/1oofujk/companies_publishing_llm_weights_on_hugging_face/ | false | false | self | 27 | {'enabled': False, 'images': [{'id': '_rEX1xvwdv17x6NFAWQpYFNONQ0BKA5Qw0Eo0JX0zWU', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/_rEX1xvwdv17x6NFAWQpYFNONQ0BKA5Qw0Eo0JX0zWU.png?width=108&crop=smart&auto=webp&s=d86627c87d9d144c16c153653adb9156be4935a0', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/_rEX1xvwdv17x6NFAWQpYFNONQ0BKA5Qw0Eo0JX0zWU.png?width=216&crop=smart&auto=webp&s=aaf13450e84c9e1f27e2080455eefb565a93ee98', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/_rEX1xvwdv17x6NFAWQpYFNONQ0BKA5Qw0Eo0JX0zWU.png?width=320&crop=smart&auto=webp&s=9f69320eccf005cf98274db64d39f1910e205ae2', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/_rEX1xvwdv17x6NFAWQpYFNONQ0BKA5Qw0Eo0JX0zWU.png?width=640&crop=smart&auto=webp&s=977c2f8c4a830d4dfa796179c0fa4c66dd3fa492', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/_rEX1xvwdv17x6NFAWQpYFNONQ0BKA5Qw0Eo0JX0zWU.png?width=960&crop=smart&auto=webp&s=0fe8d226c17b2534ef266e037ed2964e149617cb', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/_rEX1xvwdv17x6NFAWQpYFNONQ0BKA5Qw0Eo0JX0zWU.png?width=1080&crop=smart&auto=webp&s=9cd95b9a0bd050025268960365fa1e7e86c8309e', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/_rEX1xvwdv17x6NFAWQpYFNONQ0BKA5Qw0Eo0JX0zWU.png?auto=webp&s=43f171957caa9988de973025c40512017f12ebfd', 'width': 1200}, 'variants': {}}]} |
Model selection help needed | 1 | Use case: local LLM to produce evaluations of finance representatives based on uploaded reports and other data.
Hardware:
* CPU: Celeron G4930
* RAM: 16GB DDR4 (can increase if necessary)
* GPUs: 3x 3070, 5x 2070
* Power supply: 2400W
What do you guys recommend? | 2025-11-04T18:51:13 | https://www.reddit.com/r/LocalLLaMA/comments/1oofpxr/model_selection_help_needed/ | Over-Cycle5022 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oofpxr | false | null | t3_1oofpxr | /r/LocalLLaMA/comments/1oofpxr/model_selection_help_needed/ | false | false | self | 1 | null |
What are the most relevant agentic AI frameworks beyond LangGraph, LlamaIndex, Toolformer, and Parlant? | 2 | I’m researching current frameworks for agentic AI — systems that enable reasoning, planning, and tool use with LLMs.
Besides LangGraph, LlamaIndex, Toolformer, and Parlant, what other frameworks or open-source projects should I explore?
I’m interested in both research prototypes and production-grade systems. | 2025-11-04T18:37:40 | https://www.reddit.com/r/LocalLLaMA/comments/1oofchr/what_are_the_most_relevant_agentic_ai_frameworks/ | Specialist_Arugula42 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oofchr | false | null | t3_1oofchr | /r/LocalLLaMA/comments/1oofchr/what_are_the_most_relevant_agentic_ai_frameworks/ | false | false | self | 2 | null |
A reproducible benchmark for energy forecasting with PatchTST, Autoformer, Informer, and classical baselines | 1 | 2025-11-04T18:33:07 | https://github.com/Cyr-Ch/energy-forecasting-bench | juanviera23 | github.com | 1970-01-01T00:00:00 | 0 | {} | 1oof7w4 | false | null | t3_1oof7w4 | /r/LocalLLaMA/comments/1oof7w4/a_reproducible_benchmark_for_energy_forecasting/ | false | false | 1 | {'images': [{'source': {'url': 'https://external-preview.redd.it/AiA83xjA64xwx7-opvatBAyjd0wgJDoMyXPn9LxiwhI.png?auto=webp&s=4b2cc371b66ddef0f536231a77db6203c77fae18', 'width': 1200, 'height': 600}, 'resolutions': [{'url': 'https://external-preview.redd.it/AiA83xjA64xwx7-opvatBAyjd0wgJDoMyXPn9LxiwhI.png?width=108&crop=smart&auto=webp&s=2b499a530762c03de0b4e8945ca5da45cd9bb6a6', 'width': 108, 'height': 54}, {'url': 'https://external-preview.redd.it/AiA83xjA64xwx7-opvatBAyjd0wgJDoMyXPn9LxiwhI.png?width=216&crop=smart&auto=webp&s=5b1655e301ca840b0e192b4dd864e421201c9b58', 'width': 216, 'height': 108}, {'url': 'https://external-preview.redd.it/AiA83xjA64xwx7-opvatBAyjd0wgJDoMyXPn9LxiwhI.png?width=320&crop=smart&auto=webp&s=d28487407accf52610b79ce8b52279006e1c894c', 'width': 320, 'height': 160}, {'url': 'https://external-preview.redd.it/AiA83xjA64xwx7-opvatBAyjd0wgJDoMyXPn9LxiwhI.png?width=640&crop=smart&auto=webp&s=b7d9202f62b32e07c9077844b15644e45b4aa3e3', 'width': 640, 'height': 320}, {'url': 'https://external-preview.redd.it/AiA83xjA64xwx7-opvatBAyjd0wgJDoMyXPn9LxiwhI.png?width=960&crop=smart&auto=webp&s=5c0dc07b1c54d61b1bf66125819ab35fe9992396', 'width': 960, 'height': 480}, {'url': 'https://external-preview.redd.it/AiA83xjA64xwx7-opvatBAyjd0wgJDoMyXPn9LxiwhI.png?width=1080&crop=smart&auto=webp&s=0d985423b3a122dc2ac172db5927b4c81462cd77', 'width': 1080, 'height': 540}], 'variants': {}, 'id': 'AiA83xjA64xwx7-opvatBAyjd0wgJDoMyXPn9LxiwhI'}], 'enabled': False} | ||
Extropics TPU?? | 0 | Hey guys, here is a YouTube video I recently watched by David Shapiro. Didn't really understand most things that were being said... Can anyone translate this for me lol?
What are TPUs and why are they revolutionary?
https://youtu.be/mNw7KLN7raU?si=Z0W7NdScI9yTpQEh | 2025-11-04T17:59:08 | https://www.reddit.com/r/LocalLLaMA/comments/1ooe91o/extropics_tpu/ | Excellent_Koala769 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1ooe91o | false | null | t3_1ooe91o | /r/LocalLLaMA/comments/1ooe91o/extropics_tpu/ | false | false | self | 0 | {'enabled': False, 'images': [{'id': 'iz3YuhXwwKRBVxRpMiSwQGVZIdbk3qKGW1yj7zInN6Q', 'resolutions': [{'height': 81, 'url': 'https://external-preview.redd.it/iz3YuhXwwKRBVxRpMiSwQGVZIdbk3qKGW1yj7zInN6Q.jpeg?width=108&crop=smart&auto=webp&s=f1ab276319c2e567dc3141c499f082e3c66a357a', 'width': 108}, {'height': 162, 'url': 'https://external-preview.redd.it/iz3YuhXwwKRBVxRpMiSwQGVZIdbk3qKGW1yj7zInN6Q.jpeg?width=216&crop=smart&auto=webp&s=992fe632edd3879a04f156f954febe6856618e78', 'width': 216}, {'height': 240, 'url': 'https://external-preview.redd.it/iz3YuhXwwKRBVxRpMiSwQGVZIdbk3qKGW1yj7zInN6Q.jpeg?width=320&crop=smart&auto=webp&s=e23f81d506d7bc7409c5faa5e295e9215dae54b1', 'width': 320}], 'source': {'height': 360, 'url': 'https://external-preview.redd.it/iz3YuhXwwKRBVxRpMiSwQGVZIdbk3qKGW1yj7zInN6Q.jpeg?auto=webp&s=43f2a4c8564c49956f1441406a5764c394acaa51', 'width': 480}, 'variants': {}}]} |
What is the best model application for RX 7900 GRE? | 1 | Im totally new to selfhosting. I would love to use my gaming pc with a 7900 GRE instead of keeping to pay OpenAI.
What is the best interface for normal users? Is it llama.ccp?
And what model would you guys recommend to a newbie for normal tasks and for coding? | 2025-11-04T17:53:27 | https://www.reddit.com/r/LocalLLaMA/comments/1ooe3mt/what_is_the_best_model_application_for_rx_7900_gre/ | Daalex20 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1ooe3mt | false | null | t3_1ooe3mt | /r/LocalLLaMA/comments/1ooe3mt/what_is_the_best_model_application_for_rx_7900_gre/ | false | false | self | 1 | null |
xandAI-CLI Now Lets You Access Your Shell from the Browser and Run LLM Chains | 3 | 2025-11-04T17:40:24 | https://www.reddit.com/r/LocalLLaMA/comments/1oodqun/xandaicli_now_lets_you_access_your_shell_from_the/ | Sea-Reception-2697 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oodqun | false | null | t3_1oodqun | /r/LocalLLaMA/comments/1oodqun/xandaicli_now_lets_you_access_your_shell_from_the/ | false | false | 3 | null | ||
Why I love the Nvidia L4 | 0 | **TLDR:** The L4 is perfect for adding local inference capabilities to existing server infrastructure.
# Background
I started playing around with AI at home a couple years ago with a GTX 1080 and 1080ti. Mostly a handful of smaller 4B-7B LLMs, Blue Iris object detection, and an Obico server to monitor my 3D prints for failures.
It was mostly just a hobby, but I started seeing real potential to integrate it at work about a year ago. I got approval to buy an Nvidia A2 16GB to build some proof-of-concepts for our workflow.
While 16GB isn't much, it was enough to do actual useful work with Llama 3.1 8b and Qwen 2.5 14B. However, I could see a huge difference in the quality when using 32b or 72b models (albeit much slower due to being partially offloaded to CPU).
# Inference on a (power) budget
I did a bit more research and recommended we get at least 64GB combined VRAM to run the larger models, but we had two major restrictions:
1. Needed to stay in power budget constraints of our UPS's and 20A circuit.
2. Needed to run as a VM on our existing server infrastructure of 3x PowerEdge r740xd servers rather than building/buying a new server (which would require additional VMware licensing)
I didn't mind compromising a bit of speed for higher VRAM density, and this is where the L4 really shines. We paid about $2k/ea which seems steep, but in return we get:
* 24GB VRAM
* 75w TDP (no auxiliary power cable needed)
* Single slot (full-height or low-profile)
* Passively cooled
I was easily able to fit 3x GPUs in a single server for \~72GB combined VRAM, and I'm pretty sure there's room for at least one more.
I'm currently passing all 3 GPUs through to a Debian VM and running our stack with docker compose. Everything worked exactly as expected and we've been able to continue integrating local LLMs into our workflow more and more.
# Performance and model lineup
So far, the only downside is that the inference speed is a bit slower than I had hoped, especially on the larger dense models. However, the new MoE models coming out are perfectly suited for these cards. Here's an example of what we're running with llama-swap:
Card 1 stays loaded with:
* gpt-oss-20b-F16 (unsloth) @ 90k ctx
* Qwen/Qwen3-Embedding-0.6B @ 2048 ctx
* BAAI/bge-reranker-v2-m3 @ 2048 ctx
Cards 2/3 llama-swap between:
* Qwen3-Coder-30B-A3B (unsloth) UD-Q8 @ 90k ctx
* gpt-oss-120b (unsloth) @ 90k ctx (offloading some experts to CPU)
* Any other models we feel like testing out.
gpt-oss 20b is a great all-around model and runs 50t/s+ for most prompts. It's one of the best models I've tried for summarizing, researching, calling tools and answering basic questions. It's also locked in as the dedicated "task model" in Open WebUI (since calling 120b to generate a chat title is overkill and takes forever).
Qwen 3 Coder works great with Cline as long as it's running with F16 K/V cache. It easily clears 50+ t/s on short prompts, and slows to about 20t/s @ 60k, which is definitely still manageable. I've been using it to help refactor some old codebases and it's saved me several days worth of coding time. I might be able to squeeze more out with VLLM but I haven't tried that yet.
gpt-oss 120b also puts out a respectable 20t/s on short prompts, which is great for the occasional question that requires more complex problem solving.
# Looking forward
After demonstrating the viability of local LLM at work, I'm hoping we can budget for a dedicated GPU server down the road. The R6000 Blackwell Max-Q looks very appealing.
I'd also love to see a Blackwell iteration on the L4's package to get that sweet FP4 acceleration, but I'm not holding my breath as this doesn't seem to be a big target market for Nvidia.
I'm curious to hear if anyone else is running a similar setup, or if you think I should have gone a different route from the beginning. Comments welcome! | 2025-11-04T17:26:34 | https://www.reddit.com/r/LocalLLaMA/comments/1oodd98/why_i_love_the_nvidia_l4/ | AvocadoArray | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oodd98 | false | null | t3_1oodd98 | /r/LocalLLaMA/comments/1oodd98/why_i_love_the_nvidia_l4/ | false | false | self | 0 | null |
Newbie with Intel ARC B580 that want to learn LLM | 1 | Hello there, first time posting here. Sorry if there's any typo or something similar, im using my phone.
So straight to the point, not to long ago i build my pc with intel arc b580 as it's gpu. And recently i got my interest on LLM, and i tried to make one myself using phi3 model. At first it run on cpu, but after using vulkan it run on gpu. Only one day tho as the next day idk what i did but it giving error message.
So no im kinda optimistic, and want to continue to learn deeper, but gpt said that to finetune the ai it is recommended to do it with nvidiac as it have CUDA in it. And continuing with my intel would be a tough path.
So, got any tips or suggestions for me? My only guiding light is gpt and youtube so i can't really ask anyone else. | 2025-11-04T17:17:12 | https://www.reddit.com/r/LocalLLaMA/comments/1ood43l/newbie_with_intel_arc_b580_that_want_to_learn_llm/ | RahaL1La | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1ood43l | false | null | t3_1ood43l | /r/LocalLLaMA/comments/1ood43l/newbie_with_intel_arc_b580_that_want_to_learn_llm/ | false | false | self | 1 | null |
Why does it seem like GGUF files are not as popular as others? | 20 | I feel like it’s the easiest to setup and it’s been around since the beginning I believe, why does it seem like HuggingFace mainly focuses on Transformers, vLLM, etc which don’t support GGUF | 2025-11-04T17:13:43 | https://www.reddit.com/r/LocalLLaMA/comments/1ood0kn/why_does_it_seem_like_gguf_files_are_not_as/ | XiRw | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1ood0kn | false | null | t3_1ood0kn | /r/LocalLLaMA/comments/1ood0kn/why_does_it_seem_like_gguf_files_are_not_as/ | false | false | self | 20 | null |
Laptop with minimal resources | 1 | Kinda new to running these models and can't seem to get anything other than the 4b models to load. I'm running the Llama app on my windows laptop with only 16gig of RAM. Are their tricks I'm missing or am I stuck with only the smallest of models?
TIA | 2025-11-04T17:05:43 | https://www.reddit.com/r/LocalLLaMA/comments/1oocsap/laptop_with_minimal_resources/ | InReasonNotFish | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oocsap | false | null | t3_1oocsap | /r/LocalLLaMA/comments/1oocsap/laptop_with_minimal_resources/ | false | false | self | 1 | null |
Could you guys recommend the best web search API for function tool? | 5 | I use gpt-oss-120b locally and I want to give it a web search function. Duckduckgo is free but it has limited usage, and does not work well. Tavily is also free for some extent each month, but I'm worried about the privacy issue.
Are there any web search API I could connect to the model, which is free and has no-privacy-issue? | 2025-11-04T17:04:03 | https://www.reddit.com/r/LocalLLaMA/comments/1oocqk1/could_you_guys_recommend_the_best_web_search_api/ | GreedyDamage3735 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oocqk1 | false | null | t3_1oocqk1 | /r/LocalLLaMA/comments/1oocqk1/could_you_guys_recommend_the_best_web_search_api/ | false | false | self | 5 | null |
Running MiniMax-M2 locally - Existing Hardware Advice | 6 | Hi guys, I really want to run this model on Q6\_K\_XL (194 GB) by Unsloth or perhaps one of the AWQ \\ FP8 Quants.
My setup is complex though, I have two servers:
Server A -
4 x RTX 3090
1900x ThreadRipper
64GB of DDR4 RAM. ( 2133 MT/s ) - Quad Channel
Server B -
2 x RTX 3090
2 x CPUs, each Xeon E5-2695-v4
512GB of DDR4 ECC RAM ( 2133 MT/s ) - Quad Channel per CPU
\*( total 8 channels if using both Numa nodes or 4 Channels if using 1 )
I have another, 7th 3090 on my main work PC, I could throw it in somewhere if it made a difference, but prefer to get it done with 6.
I can't place all 6 GPUs on Server B, as it is not supporting MoBo PCIe bifurcation, and does not have enough PCIe Lanes for all 6 GPUs alongside the other PCIe cards ( NVMe storage over PCIe and NIC ).
I CAN place all 6 GPUs on Server A but the most RAM that can be placed on this server is 128GB, MoBo limitation.
I know there are technologies out there such as RAY that would allow me to POOL both Servers GPUs together via network ( I have 40Gbps Network so plenty fast for inference ), but I don't know if RAY will even work in my setup, even if I balance 3 GPUs on each Server, for PP i need ( 1, 2, 4, 8, ... per server. ). Can I do PP2 on server A and PP4 on ServerB ?!..
Even if I would get PP to work with Ray, would I still be able to also offload to RAM of Server B ?
Ideally I would want to use all 6 GPUs for maximum vRAM of 144GB for KV & Some of the weight, and add \~100GB in weights from RAM. ( I also need full context - I'm a software engineer ).
Last, if I can't get 15 t/s+ inference and 1000 t/s+ prompt processing, it won't suffice, as I need it for agentic work and agentic coding.
What do you guys think?
If not doable with said hardware, would you recommend I upgrade my Mothboard & CPU to a 7xx2/3 Epyc \*( utilizing the same RAM) for increased offloading speeds or go for more GPUs and cheaper motherboard but one that has pcie-bifurcation to have say 8-10 x RTX 3090 GPUs on the same RIG ? If I can fit the model in GPU, I don't need the RAM or memory channels eitherway. | 2025-11-04T16:53:01 | https://www.reddit.com/r/LocalLLaMA/comments/1oocfc4/running_minimaxm2_locally_existing_hardware_advice/ | BigFoxMedia | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oocfc4 | false | null | t3_1oocfc4 | /r/LocalLLaMA/comments/1oocfc4/running_minimaxm2_locally_existing_hardware_advice/ | false | false | self | 6 | null |
Cache-to-Cache (C2C) | 97 |
A new framework, Cache-to-Cache (C2C), lets multiple LLMs communicate directly through their KV-caches instead of text, transferring deep semantics without token-by-token generation.
It fuses cache representations via a neural projector and gating mechanism for efficient inter-model exchange.
The payoff: up to 10% higher accuracy, 3–5% gains over text-based communication, and 2× faster responses.
Cache-to-Cache: Direct Semantic Communication Between Large Language Models
Code: https://github.com/thu-nics/C2C
Project: https://github.com/thu-nics
Paper: https://arxiv.org/abs/2510.03215
> In my opinion: can also probably be used instead of thinking word tokens | 2025-11-04T16:49:08 | https://www.reddit.com/r/LocalLLaMA/comments/1oocbmd/cachetocache_c2c/ | xXWarMachineRoXx | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oocbmd | false | null | t3_1oocbmd | /r/LocalLLaMA/comments/1oocbmd/cachetocache_c2c/ | false | false | self | 97 | {'enabled': False, 'images': [{'id': 'O5-vC-DVYKdVTris6nMBcXIKLdesvUodSZLnUIG9P_M', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/O5-vC-DVYKdVTris6nMBcXIKLdesvUodSZLnUIG9P_M.png?width=108&crop=smart&auto=webp&s=41c0c7103892b9c40fa9fb8bd2f13250716e72ab', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/O5-vC-DVYKdVTris6nMBcXIKLdesvUodSZLnUIG9P_M.png?width=216&crop=smart&auto=webp&s=f2c4db6e194859d8163cec78c7afebf027090443', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/O5-vC-DVYKdVTris6nMBcXIKLdesvUodSZLnUIG9P_M.png?width=320&crop=smart&auto=webp&s=785f6cb6867c4ddd60b12684c6c7471491ab85fd', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/O5-vC-DVYKdVTris6nMBcXIKLdesvUodSZLnUIG9P_M.png?width=640&crop=smart&auto=webp&s=fd6180bd235777470f4769b8abacbdf5cc30bf05', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/O5-vC-DVYKdVTris6nMBcXIKLdesvUodSZLnUIG9P_M.png?width=960&crop=smart&auto=webp&s=17b3e7fce4dea214cadd5946882afd0838f0d759', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/O5-vC-DVYKdVTris6nMBcXIKLdesvUodSZLnUIG9P_M.png?width=1080&crop=smart&auto=webp&s=a634d3f31e6f27f701019eb2a1fc5cb9dc5f60e4', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/O5-vC-DVYKdVTris6nMBcXIKLdesvUodSZLnUIG9P_M.png?auto=webp&s=8bdd043ff60f54dcb24ca2f2bfe8f699800f30d0', 'width': 1200}, 'variants': {}}]} |
[\MAINTENANCE] ? | 0 | Is GLM døwn?
It's been giving me this message for a while.
This happened mid-session, by the way. | 2025-11-04T16:34:31 | Darkenned_Hand_Eyes | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1oobxe1 | false | null | t3_1oobxe1 | /r/LocalLLaMA/comments/1oobxe1/maintenance/ | false | false | 0 | {'enabled': True, 'images': [{'id': '8o0SAn_3eITycQY7wPibkAIBSp2SN5tL0goDtf04GHQ', 'resolutions': [{'height': 42, 'url': 'https://preview.redd.it/m6ciqm6lp9zf1.jpeg?width=108&crop=smart&auto=webp&s=653ff970a6d81f4449d6b87e4ed607fb5a07e1d9', 'width': 108}, {'height': 84, 'url': 'https://preview.redd.it/m6ciqm6lp9zf1.jpeg?width=216&crop=smart&auto=webp&s=b34951c5f3f09d7ee95f5066e0a6ba15b35afb8a', 'width': 216}, {'height': 125, 'url': 'https://preview.redd.it/m6ciqm6lp9zf1.jpeg?width=320&crop=smart&auto=webp&s=be748f9c305d75a0b430ff8e4daf01302eb9f333', 'width': 320}, {'height': 251, 'url': 'https://preview.redd.it/m6ciqm6lp9zf1.jpeg?width=640&crop=smart&auto=webp&s=f0a58f689abad202610d6f3bb6269215b715dfde', 'width': 640}], 'source': {'height': 282, 'url': 'https://preview.redd.it/m6ciqm6lp9zf1.jpeg?auto=webp&s=96d34c59647eb2ac93c30bf01e597822af41f627', 'width': 719}, 'variants': {}}]} | ||
Help Identify and link this Kokoro TTS version. | 1 | I saw this video somewhere, but i couldn't find the Kokoro TTS version anywhere, the guy who posted this video is gatekeeping.
https://preview.redd.it/59d3gzhbp9zf1.png?width=1306&format=png&auto=webp&s=101ace96131b7c90705d911c9fc071589d9c7797
| 2025-11-04T16:33:12 | https://www.reddit.com/r/LocalLLaMA/comments/1oobw2h/help_identify_and_link_this_kokoro_tts_version/ | ahtishamafzal | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oobw2h | false | null | t3_1oobw2h | /r/LocalLLaMA/comments/1oobw2h/help_identify_and_link_this_kokoro_tts_version/ | false | false | 1 | null | |
Dynamic LLM generated UI | 3 | In the world of AI, UI's need to be dynamic. I gave the LLM full control of what it wants to generate unlike AI SDK where the UI is generated by function calling. I plan to make it open source when I am complete (there is a lot to work on).
Ask me anything!!
https://reddit.com/link/1oobqzx/video/yr7dr2h1o9zf1/player
https://preview.redd.it/iyjndhico9zf1.png?width=1892&format=png&auto=webp&s=27549c62ed4a3d7b539c2049c0561181e15c35b0 | 2025-11-04T16:27:54 | https://www.reddit.com/r/LocalLLaMA/comments/1oobqzx/dynamic_llm_generated_ui/ | ItzCrazyKns | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oobqzx | false | null | t3_1oobqzx | /r/LocalLLaMA/comments/1oobqzx/dynamic_llm_generated_ui/ | false | false | 3 | null | |
PewDiePie running local LLMs on $20k GPU home set-up | 0 | 2025-11-04T16:19:34 | https://www.youtube.com/watch?v=qw4fDU18RcU&t=3s | MorroWtje | youtube.com | 1970-01-01T00:00:00 | 0 | {} | 1oobit6 | false | {'oembed': {'author_name': 'PewDiePie', 'author_url': 'https://www.youtube.com/@PewDiePie', 'height': 200, 'html': '<iframe width="356" height="200" src="https://www.youtube.com/embed/qw4fDU18RcU?start=3&feature=oembed&enablejsapi=1" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen title="STOP. Using AI Right now"></iframe>', 'provider_name': 'YouTube', 'provider_url': 'https://www.youtube.com/', 'thumbnail_height': 360, 'thumbnail_url': 'https://i.ytimg.com/vi/qw4fDU18RcU/hqdefault.jpg', 'thumbnail_width': 480, 'title': 'STOP. Using AI Right now', 'type': 'video', 'version': '1.0', 'width': 356}, 'type': 'youtube.com'} | t3_1oobit6 | /r/LocalLLaMA/comments/1oobit6/pewdiepie_running_local_llms_on_20k_gpu_home_setup/ | false | false | 0 | {'enabled': False, 'images': [{'id': 'WddxiFHLc3dMB9LBPGHmNWXXrzglB78uxpSOk1Y4d6E', 'resolutions': [{'height': 81, 'url': 'https://external-preview.redd.it/WddxiFHLc3dMB9LBPGHmNWXXrzglB78uxpSOk1Y4d6E.jpeg?width=108&crop=smart&auto=webp&s=81ae433e5b52cd3bb13282d643d5efae83d1a280', 'width': 108}, {'height': 162, 'url': 'https://external-preview.redd.it/WddxiFHLc3dMB9LBPGHmNWXXrzglB78uxpSOk1Y4d6E.jpeg?width=216&crop=smart&auto=webp&s=fb1b146656366b57a026157f93d1ab087ae3ad27', 'width': 216}, {'height': 240, 'url': 'https://external-preview.redd.it/WddxiFHLc3dMB9LBPGHmNWXXrzglB78uxpSOk1Y4d6E.jpeg?width=320&crop=smart&auto=webp&s=d41e205151bfdcec37d1be377abc09d05a02773e', 'width': 320}], 'source': {'height': 360, 'url': 'https://external-preview.redd.it/WddxiFHLc3dMB9LBPGHmNWXXrzglB78uxpSOk1Y4d6E.jpeg?auto=webp&s=4b896f176b81cab769e7def8a5b3b7fe4a6b8eff', 'width': 480}, 'variants': {}}]} | ||
how to prepare dataset for fine tunining (llama 3.2 8b) | 5 | Hello everyone,
I’m about to try fine-tuning a Llama 8B 3.2 model. This model should be able to use tools, which I intend to enable.
My biggest concern is how to prepare the dataset so that it’s “correct” for Llama. At the moment I’m using this structure:
<|begin_of_text|><|start_header_id|>user<|end_header_id|>
[natural language question]
<|eot_id|><|start_header_id|>assistant<|end_header_id|>
<reasoning>
[detailed analytical reasoning]
</reasoning>
<sql>
[SQL query]
</sql>
{"name": "sql_query_executor", "parameters": {"sql_query": "[SQL query here]"}}
<|eot_id|>
<|start_header_id|>ipython<|end_header_id|> with the tabular results, followed by a final interpretative comment from the assistant.
Here’s an example (it’s in Italian but I will translate it into English):
{"text": "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\nFor each organizational unit in 2024, which expenditure chapters generated initial residuals higher than final residuals (i.e. those that have been settled)?\n<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n<reasoning>\nThe goal is to identify the expenditure chapters where the institution managed to reduce accumulated debt, meaning cases where the liquidation of past obligations exceeded the accumulation of new residual debts. This is a positive indicator of credit management.\n\nThe user asks for “initial residuals higher than final residuals,” which identifies a condition of debt reduction. Reviewing the available descriptions:\n- VISTA_BILANCIO_SPESA_AI: the description specifies that it “collects structured data related to expenditure items of the financial budget” and that “each record describes a specific expenditure item, including identifying elements, functional and economic classifications, organizational references, and financial values related to allocations, variations, commitments, and payments.”\n- VISTA_BILANCIO_ENTRATA_AI: the description specifies that it “contains structured data related to the revenue items of the financial budget.”\n\nSince the question explicitly concerns expenditure “residuals,” the VISTA_BILANCIO_SPESA_AI view is the correct source, as its description explicitly includes the tracking of residuals.\n\nI need to identify which column represents the “initial residuals.” Scanning the view’s columns, I find RESIDUI_INIZIALI, described as “Indicates expenditure committed in previous years and not yet paid at the beginning of the current fiscal year.” It represents the debt carried over from previous periods.\n\nNext, I need to identify which column represents the “final residuals.” Scanning the columns, I find RESIDUI_PRESUNTI, described as “Estimate of expenditure committed and unpaid that is expected to be carried over to the following fiscal year.” It represents the estimated remaining debt.\n\nI need to identify which column represents the “organizational unit.” Scanning the columns, I find UNITA_ORGANIZZATIVA, described as “Indicates the operational structure of the entity responsible for managing the budget chapter,” with a related field RESPONSABILE_UO for the name of the responsible person.\n\nI need to identify which column represents the “chapter.” Scanning the columns, I find CAPITOLO, described as “the elementary unit of the budget.”\n\nI need to identify which column represents the “year.” Scanning the columns, I find ESERCIZIO.\n\nTo obtain the requested result, I therefore need to:\n- filter by ESERCIZIO = 2024;\n- filter by TIPO = 'E' (actual data);\n- filter by RESIDUI_INIZIALI > RESIDUI_PRESUNTI (condition indicating net liquidation);\n- group by UNITA_ORGANIZZATIVA;\n- include details of DESCRIZIONE_CAP, CAPITOLO, RESIDUI_INIZIALI, RESIDUI_PRESUNTI for traceability;\n- calculate the residual reduction for each chapter.\n</reasoning>\n\n<sql>\nSELECT\n UNITA_ORGANIZZATIVA,\n CAPITOLO,\n DESCRIZIONE_CAP,\n ROUND(RESIDUI_INIZIALI, 2) AS RESIDUI_INIZIO_ESERCIZIO,\n ROUND(RESIDUI_PRESUNTI, 2) AS RESIDUI_FINE_ESERCIZIO,\n ROUND(RESIDUI_INIZIALI - RESIDUI_PRESUNTI, 2) AS RIDUZIONE_RESIDUI,\n ROUND((RESIDUI_INIZIALI - RESIDUI_PRESUNTI) / NULLIF(RESIDUI_INIZIALI, 0) * 100, 2) AS PERCENTUALE_LIQUIDAZIONE\nFROM VISTA_BILANCIO_SPESA_AI\nWHERE ESERCIZIO = 2024\n AND TIPO = 'E'\n AND RESIDUI_INIZIALI > 0\n AND RESIDUI_INIZIALI > RESIDUI_PRESUNTI\n AND UNITA_ORGANIZZATIVA IS NOT NULL\nORDER BY UNITA_ORGANIZZATIVA, RIDUZIONE_RESIDUI DESC\nFETCH FIRST 50 ROWS ONLY;\n</sql>\n\n{\"name\": \"sql_query_executor\", \"parameters\": {\"sql_query\": \"SELECT UNITA_ORGANIZZATIVA, CAPITOLO, DESCRIZIONE_CAP, ROUND(RESIDUI_INIZIALI, 2) AS RESIDUI_INIZIO_ESERCIZIO, ROUND(RESIDUI_PRESUNTI, 2) AS RESIDUI_FINE_ESERCIZIO, ROUND(RESIDUI_INIZIALI - RESIDUI_PRESUNTI, 2) AS RIDUZIONE_RESIDUI, ROUND((RESIDUI_INIZIALI - RESIDUI_PRESUNTI) / NULLIF(RESIDUI_INIZIALI, 0) * 100, 2) AS PERCENTUALE_LIQUIDAZIONE FROM VISTA_BILANCIO_SPESA_AI WHERE ESERCIZIO = 2024 AND TIPO = 'E' AND RESIDUI_INIZIALI > 0 AND RESIDUI_INIZIALI > RESIDUI_PRESUNTI AND UNITA_ORGANIZZATIVA IS NOT NULL ORDER BY UNITA_ORGANIZZATIVA, RIDUZIONE_RESIDUI DESC FETCH FIRST 50 ROWS ONLY;\"}}<|eot_id|><|start_header_id|>ipython<|end_header_id|>\n\n{\"columns\": [\"UNITA_ORGANIZZATIVA\", \"CAPITOLO\", \"DESCRIZIONE_CAP\", \"RESIDUI_INIZIO_ESERCIZIO\", \"RESIDUI_FINE_ESERCIZIO\", \"RIDUZIONE_RESIDUI\", \"PERCENTUALE_LIQUIDAZIONE\"], \"data\": [[\"ASSISTENZA ANZIANI E DISABILI\", \"1020205001100\", \"MANUTENZIONE ORDINARIA IMMOBILI\", 125000.00, 85000.00, 40000.00, 32.00], [\"SERVIZI GENERALI E LEGALI\", \"1050103000100\", \"UTENZE E CANONI PER ENERGIA ELETTRICA\", 95000.00, 45000.00, 50000.00, 52.63], [\"UFFICIO TECNICO\", \"2010401000400\", \"CONTRATTI DI SERVIZIO\", 210000.00, 155000.00, 55000.00, 26.19], [\"GESTIONE IMPIANTI SPORT\", \"1020304001500\", \"ASSISTENZA INFORMATICA E MANUTENZIONE\", 75000.00, 22500.00, 52500.00, 70.00]], \"row_count\": 4}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nThe analysis identifies the expenditure chapters where, in 2024, the institution made significant efforts to settle residual debt. The “Sports Facilities Management” unit stands out with the best performance, liquidating 70% of its initial residuals (€52,500 out of €75,000), demonstrating particular efficiency in settling IT maintenance payments. “General and Legal Services” also performed well with a 52.63% liquidation rate (€50,000), showing acceleration in utility payments. The “Technical Office,” despite managing the largest absolute reduction (€55,000), shows a 26.19% liquidation rate, indicating substantial residuals accumulated previously that still require further clearance. These differences suggest disparities in financial management capacity among organizational units, with implications for the entity’s overall financial cycle.<|eot_id|>"}
I’d like you to confirm whether the use of the tags is correct for fine-tuning.
I’ll keep the system part the same for all examples since I’m specializing it for a specific database.
n the system prompt, I mean to include some natural language instructions + the database schema + the tool’s JSON schema
Does it look correct to you?
Any suggestions?
Thanks. | 2025-11-04T15:43:31 | https://www.reddit.com/r/LocalLLaMA/comments/1ooajcl/how_to_prepare_dataset_for_fine_tunining_llama_32/ | Juno9419 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1ooajcl | false | null | t3_1ooajcl | /r/LocalLLaMA/comments/1ooajcl/how_to_prepare_dataset_for_fine_tunining_llama_32/ | false | false | self | 5 | null |
llama.cpp releases new official WebUI | 959 | 2025-11-04T15:26:30 | https://github.com/ggml-org/llama.cpp/discussions/16938 | paf1138 | github.com | 1970-01-01T00:00:00 | 0 | {} | 1ooa342 | false | null | t3_1ooa342 | /r/LocalLLaMA/comments/1ooa342/llamacpp_releases_new_official_webui/ | false | false | 959 | {'enabled': False, 'images': [{'id': '3mPqb7hXnKE3QMeOYvmnNH3HEJEfsY-FkGb0pZ8tDhU', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/3mPqb7hXnKE3QMeOYvmnNH3HEJEfsY-FkGb0pZ8tDhU.png?width=108&crop=smart&auto=webp&s=df08f91c7fb67a4909e102d89277a1e35da547ee', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/3mPqb7hXnKE3QMeOYvmnNH3HEJEfsY-FkGb0pZ8tDhU.png?width=216&crop=smart&auto=webp&s=98a33e7ea187958ef637bfaed12a66808bc31e2c', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/3mPqb7hXnKE3QMeOYvmnNH3HEJEfsY-FkGb0pZ8tDhU.png?width=320&crop=smart&auto=webp&s=62a56eb206281110a380de2bad8b1912c61d0f36', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/3mPqb7hXnKE3QMeOYvmnNH3HEJEfsY-FkGb0pZ8tDhU.png?width=640&crop=smart&auto=webp&s=23cd274a23b9ffd23182c3f9522388baf8354b97', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/3mPqb7hXnKE3QMeOYvmnNH3HEJEfsY-FkGb0pZ8tDhU.png?width=960&crop=smart&auto=webp&s=6218cbddc20719e53bc50e6fcc7942573399bcfc', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/3mPqb7hXnKE3QMeOYvmnNH3HEJEfsY-FkGb0pZ8tDhU.png?width=1080&crop=smart&auto=webp&s=2bb23045e0bf16ce89edbae92ff69445c3d333d2', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/3mPqb7hXnKE3QMeOYvmnNH3HEJEfsY-FkGb0pZ8tDhU.png?auto=webp&s=1af622a15b4014c9ca7ebcd3eb92b4092549b334', 'width': 1200}, 'variants': {}}]} | ||
I fine-tuned (SFT) a 14B model on a free Colab session just using TRL | 10 | I've put together a notebook that runs on a **free Colab (T4 GPU)** and lets you fine-tune models up to **14B parameters** 🤯
It only uses **TRL**, which now includes new memory optimizations that make this possible. In the example, I fine-tune a reasoning model that generates *reasoning traces,* and adapt it to produce these traces in different languages depending on the user’s request.
Notebook: [https://colab.research.google.com/github/huggingface/trl/blob/main/examples/notebooks/sft\_trl\_lora\_qlora.ipynb](https://colab.research.google.com/github/huggingface/trl/blob/main/examples/notebooks/sft_trl_lora_qlora.ipynb)
More TRL notebooks I also worked on:
[https://github.com/huggingface/trl/tree/main/examples/notebooks](https://github.com/huggingface/trl/tree/main/examples/notebooks)
Happy coding! :D | 2025-11-04T14:41:45 | https://www.reddit.com/r/LocalLLaMA/comments/1oo8x7d/i_finetuned_sft_a_14b_model_on_a_free_colab/ | External-Rub5414 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oo8x7d | false | null | t3_1oo8x7d | /r/LocalLLaMA/comments/1oo8x7d/i_finetuned_sft_a_14b_model_on_a_free_colab/ | false | false | self | 10 | {'enabled': False, 'images': [{'id': 'nkhh65ujo5BznFJFojoMPaKjGuLSpPj6KGhRov-ykOg', 'resolutions': [{'height': 108, 'url': 'https://external-preview.redd.it/nkhh65ujo5BznFJFojoMPaKjGuLSpPj6KGhRov-ykOg.png?width=108&crop=smart&auto=webp&s=3118973964e59402feea50688d746b67ecd3d2df', 'width': 108}, {'height': 216, 'url': 'https://external-preview.redd.it/nkhh65ujo5BznFJFojoMPaKjGuLSpPj6KGhRov-ykOg.png?width=216&crop=smart&auto=webp&s=0e2f90964c81a1de52938be6bcb08665605293f2', 'width': 216}], 'source': {'height': 260, 'url': 'https://external-preview.redd.it/nkhh65ujo5BznFJFojoMPaKjGuLSpPj6KGhRov-ykOg.png?auto=webp&s=3ea22acc6f5634a7b861b56e2c98736d10235554', 'width': 260}, 'variants': {}}]} |
made a simple webui that supports Qwen3VL for fun | 1 | https://reddit.com/link/1oo8vgs/video/xyl1di3w49zf1/player
Uses the llama-server endpoint and hopefully it inspires people to make their own webui. | 2025-11-04T14:39:54 | https://www.reddit.com/r/LocalLLaMA/comments/1oo8vgs/made_a_simple_webui_that_supports_qwen3vl_for_fun/ | Odd-Ordinary-5922 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oo8vgs | false | null | t3_1oo8vgs | /r/LocalLLaMA/comments/1oo8vgs/made_a_simple_webui_that_supports_qwen3vl_for_fun/ | false | false | self | 1 | null |
[Research] Cross-Stage Vulnerabilities in Large Language Model Architectures | 11 | Hey everyone
I did some research and just put a paper on arXiv. It looks at systemic security flaws in LLMs not just the usual filter bypasses.
The main problem I found is what I call Unvalidated Trust. The AI basically trusts its own internal steps blindly.
This means you can trick it.
I found 41 patterns. I'd be interested if you guys can replicate or test some of them.
Here are a few of the key findings:
• The Poem (Section 8.4): I found you can hide a malicious command like deleting files in a poem. The models even GPT-4o just generate the code. They seem to care more about the aesthetic form than the harmful content.
• Implicit Command (Section 8.21): This is the wildest one. You can get a model to generate malicious code just from the structure of data. The prompt never says execute or run. The data structure itself is seen as the command.
• Memory (Section 8.27): You can plant a sleeper rule in the chat memory. Many turns later you use a normal-looking word and it triggers the hidden rule to run a new harmful command.
Let me know what you think.
Heres the paper: https://arxiv.org/abs/2510.27190 | 2025-11-04T14:33:51 | https://arxiv.org/abs/2510.27190 | Solid-Tomorrow6548 | arxiv.org | 1970-01-01T00:00:00 | 0 | {} | 1oo8q0v | false | null | t3_1oo8q0v | /r/LocalLLaMA/comments/1oo8q0v/research_crossstage_vulnerabilities_in_large/ | false | false | default | 11 | null |
Question about whether I can post a link to my site for GPU prices. | 2 | I have a site I built that looks across different sources to gather GPU price information. I was wondering if it would be okay for me to post about it. | 2025-11-04T14:16:00 | https://www.reddit.com/r/LocalLLaMA/comments/1oo89rt/question_about_whether_i_can_post_a_link_to_my/ | OkIndependence3956 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oo89rt | false | null | t3_1oo89rt | /r/LocalLLaMA/comments/1oo89rt/question_about_whether_i_can_post_a_link_to_my/ | false | false | self | 2 | null |
Survey about AI News Interest | 1 | Some colleagues and I are running a survey to look at what aspects of AI news people are most interested in.
The survey results may help inform people who are thinking of starting a platform that covers AI news – hence the survey to find out what that is.
Regardless, the survey is 100% Anonymous and all results are open to the public.
If this interests you, please take the survey and share it if you get the chance.
[https://forms.gle/b2gBrwxdG8q13oxJ6](https://forms.gle/b2gBrwxdG8q13oxJ6) | 2025-11-04T14:15:53 | https://www.reddit.com/r/LocalLLaMA/comments/1oo89o8/survey_about_ai_news_interest/ | hg0428 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oo89o8 | false | null | t3_1oo89o8 | /r/LocalLLaMA/comments/1oo89o8/survey_about_ai_news_interest/ | false | false | self | 1 | {'enabled': False, 'images': [{'id': 'cVd1obPGzGxLAqzZhx6b7IIwfkYZEV97gcx8J6VgHII', 'resolutions': [{'height': 56, 'url': 'https://external-preview.redd.it/cVd1obPGzGxLAqzZhx6b7IIwfkYZEV97gcx8J6VgHII.png?width=108&crop=smart&auto=webp&s=c465c9abf39fdc5757f830621be49f352e783344', 'width': 108}, {'height': 113, 'url': 'https://external-preview.redd.it/cVd1obPGzGxLAqzZhx6b7IIwfkYZEV97gcx8J6VgHII.png?width=216&crop=smart&auto=webp&s=339659a543cd967479b4389bc014c36f73d22563', 'width': 216}, {'height': 168, 'url': 'https://external-preview.redd.it/cVd1obPGzGxLAqzZhx6b7IIwfkYZEV97gcx8J6VgHII.png?width=320&crop=smart&auto=webp&s=97f54e5f47af66c09c842e02d9b2ac6b583f1d77', 'width': 320}, {'height': 336, 'url': 'https://external-preview.redd.it/cVd1obPGzGxLAqzZhx6b7IIwfkYZEV97gcx8J6VgHII.png?width=640&crop=smart&auto=webp&s=302d0640da7632d0cc240f1ac0c0bf6abb503cdd', 'width': 640}, {'height': 504, 'url': 'https://external-preview.redd.it/cVd1obPGzGxLAqzZhx6b7IIwfkYZEV97gcx8J6VgHII.png?width=960&crop=smart&auto=webp&s=61b72cf3c4b110d3979d009741c926bc3519f5fd', 'width': 960}, {'height': 567, 'url': 'https://external-preview.redd.it/cVd1obPGzGxLAqzZhx6b7IIwfkYZEV97gcx8J6VgHII.png?width=1080&crop=smart&auto=webp&s=5f995e169d15212db4f07b64a7b5a62c75681987', 'width': 1080}], 'source': {'height': 630, 'url': 'https://external-preview.redd.it/cVd1obPGzGxLAqzZhx6b7IIwfkYZEV97gcx8J6VgHII.png?auto=webp&s=08586f8e01ce9a8259de570af75b249b8f4cd33d', 'width': 1200}, 'variants': {}}]} |
Nvidia Jetson Orin Nano Super (8 gb) Llama-bench: Qwen3-4B-Instruct-2507-Q4_0 | 6 | I'm working on an LLM-driven autonomous ground drone. My current implementation is teleoperation over my local network from my host PC. I'm exploring the viability of moving it all to the edge and just switched picked up an Nvidia Jetson Orin Nano Super.
I know there have been a few of these posts recently but I hadn't seen anything that actually list out specs and commands used for bench-marking:
**Jetson Orin Nano Super (8gb)**
* M.2 NVMe Gen3x4 SSD 256GB 2200 MBS
* Super Power Mode (profile 2) enabled
* llama.cpp built from source using latest release (6945)
​
jwest33@jwest33-desktop:~/Desktop/llama.cpp$ ./build/bin/llama-bench \
-m models/Qwen3-4B-Instruct-2507-Q4_0.gguf \
-ngl 99 \
-fa 1 \
-t 6 \
-p 128,512,1024,2048 \
-n 32,64,128,256 \
-b 2048 \
-ub 512 \
-r 3
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 1 CUDA devices:
Device 0: Orin, compute capability 8.7, VMM: yes
| model | size | params | backend | ngl | fa | test | t/s |
| ------------------------------ | ---------: | ---------: | ---------- | --: | -: | --------------: | -------------------: |
| qwen3 4B Q4_0 | 2.21 GiB | 4.02 B | CUDA | 99 | 1 | pp128 | 588.08 ± 47.70 |
| qwen3 4B Q4_0 | 2.21 GiB | 4.02 B | CUDA | 99 | 1 | pp512 | 710.32 ± 1.18 |
| qwen3 4B Q4_0 | 2.21 GiB | 4.02 B | CUDA | 99 | 1 | pp1024 | 726.05 ± 8.75 |
| qwen3 4B Q4_0 | 2.21 GiB | 4.02 B | CUDA | 99 | 1 | pp2048 | 712.74 ± 0.40 |
| qwen3 4B Q4_0 | 2.21 GiB | 4.02 B | CUDA | 99 | 1 | tg32 | 23.23 ± 0.02 |
| qwen3 4B Q4_0 | 2.21 GiB | 4.02 B | CUDA | 99 | 1 | tg64 | 23.02 ± 0.01 |
| qwen3 4B Q4_0 | 2.21 GiB | 4.02 B | CUDA | 99 | 1 | tg128 | 22.40 ± 0.07 |
| qwen3 4B Q4_0 | 2.21 GiB | 4.02 B | CUDA | 99 | 1 | tg256 | 22.98 ± 0.07 |
build: cc98f8d34 (6945)
Useless comparison of same bench run on an RTX 5090:
PS C:\Users\jwest33> llama-bench -m C:/models/Qwen3-4B-Instruct-2507/Qwen3-4B-Instruct-2507-Q4_0.gguf -ngl 99 -fa 1 -t 6 -p 128,512,1024,2048 -n 32,64,128,256 -b 2048 -ub 512 -r 3
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 1 CUDA devices:
Device 0: NVIDIA GeForce RTX 5090, compute capability 12.0, VMM: yes
load_backend: loaded CUDA backend from C:\llamacpp\ggml-cuda.dll
load_backend: loaded RPC backend from C:\llamacpp\ggml-rpc.dll
load_backend: loaded CPU backend from C:\llamacpp\ggml-cpu-alderlake.dll
| model | size | params | backend | ngl | threads | fa | test | t/s |
| ------------------------------ | ---------: | ---------: | ---------- | --: | ------: | -: | --------------: | -------------------: |
| qwen3 4B Q4_0 | 2.21 GiB | 4.02 B | CUDA | 99 | 6 | 1 | pp128 | 9083.27 ± 453.11 |
| qwen3 4B Q4_0 | 2.21 GiB | 4.02 B | CUDA | 99 | 6 | 1 | pp512 | 20304.25 ± 319.92 |
| qwen3 4B Q4_0 | 2.21 GiB | 4.02 B | CUDA | 99 | 6 | 1 | pp1024 | 21760.52 ± 360.38 |
| qwen3 4B Q4_0 | 2.21 GiB | 4.02 B | CUDA | 99 | 6 | 1 | pp2048 | 21696.48 ± 91.91 |
| qwen3 4B Q4_0 | 2.21 GiB | 4.02 B | CUDA | 99 | 6 | 1 | tg32 | 316.27 ± 4.81 |
| qwen3 4B Q4_0 | 2.21 GiB | 4.02 B | CUDA | 99 | 6 | 1 | tg64 | 295.49 ± 6.21 |
| qwen3 4B Q4_0 | 2.21 GiB | 4.02 B | CUDA | 99 | 6 | 1 | tg128 | 308.85 ± 1.60 |
| qwen3 4B Q4_0 | 2.21 GiB | 4.02 B | CUDA | 99 | 6 | 1 | tg256 | 336.04 ± 14.27 |
build: 961660b8c (6912) | 2025-11-04T14:14:54 | https://www.reddit.com/r/LocalLLaMA/comments/1oo88sq/nvidia_jetson_orin_nano_super_8_gb_llamabench/ | JEs4 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oo88sq | false | null | t3_1oo88sq | /r/LocalLLaMA/comments/1oo88sq/nvidia_jetson_orin_nano_super_8_gb_llamabench/ | false | false | self | 6 | null |
Is GPT-OSS-120B the best llm that fits in 96GB VRAM? | 89 | Hi. I wonder if gpt-oss-120b is the best local llm that can be run on 96GB VRAM GPU. Do you guys have any suggestions otherwise gpt-oss? | 2025-11-04T13:48:00 | https://www.reddit.com/r/LocalLLaMA/comments/1oo7kqy/is_gptoss120b_the_best_llm_that_fits_in_96gb_vram/ | GreedyDamage3735 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oo7kqy | false | null | t3_1oo7kqy | /r/LocalLLaMA/comments/1oo7kqy/is_gptoss120b_the_best_llm_that_fits_in_96gb_vram/ | false | false | self | 89 | null |
NVIDIA GB20 vs M4 pro/ max | 0 | Hello everyone,
my company plan to buy me a computer for inference on-site.
How does M4 pro 64GB compare to Nvidia GB20 128GB on oss-20B
Will I get more token/s on Nvidia chip ?
Thx in advance | 2025-11-04T13:27:05 | https://www.reddit.com/r/LocalLLaMA/comments/1oo72lx/nvidia_gb20_vs_m4_pro_max/ | EffectiveGlove1651 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oo72lx | false | null | t3_1oo72lx | /r/LocalLLaMA/comments/1oo72lx/nvidia_gb20_vs_m4_pro_max/ | false | false | self | 0 | null |
How to speed up diarization speed for WhisperX? | 2 | I am currently encountering diarization speed issue for WhisperX.
Based on [https://github.com/m-bain/whisperX/issues/499](https://github.com/m-bain/whisperX/issues/499) , the possible reason is diarization is executing on CPU.
I have tried the mentioned workaround. This is my Dockerfile, running on runpod.
FROM runpod/pytorch:cuda12
# Set the working directory in the container
WORKDIR /app
# Install ffmpeg, vim
RUN apt-get update && \
apt-get install -y ffmpeg vim
# Install WhisperX via pip
RUN pip install --upgrade pip && \
pip install --no-cache-dir runpod==1.7.7 whisperx==3.3.1 pyannote.audio==3.3.2 torchaudio==2.8.0 matplotlib==3.10.7
# https://github.com/m-bain/whisperX/issues/499
RUN pip uninstall -y onnxruntime && \
pip install --force-reinstall --no-cache-dir onnxruntime-gpu
# Download large-v3 model
RUN python -c "import whisperx; whisperx.load_model('large-v3', device='cpu', compute_type='int8')"
# Initialize diarization pipeline
RUN python -c "import whisperx; whisperx.DiarizationPipeline(use_auth_token='xxx', device='cpu')"
# Copy source code into image
COPY src src
# -u disables output buffering so logs appear in real-time.
CMD [ "python", "-u", "src/handler.py" ]
This is my Python code.
print(f"🤖 whisperx.DiarizationPipeline done: {time_s:.2f} s") import runpod
import whisperx
import time
start_time = time.time()
diarize_model = whisperx.DiarizationPipeline(
use_auth_token='...',
device='cuda'
)
end_time = time.time()
time_s = (end_time - start_time)
For a one minute transcription, it will also took one minute to perform the diarization, which I feel is pretty slow.
diarize_segments = diarize_model(audio)
I was wondering, what else I can try, to speed up the diarization process?
Thank you. | 2025-11-04T13:17:35 | https://www.reddit.com/r/LocalLLaMA/comments/1oo6ud3/how_to_speed_up_diarization_speed_for_whisperx/ | yccheok | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oo6ud3 | false | null | t3_1oo6ud3 | /r/LocalLLaMA/comments/1oo6ud3/how_to_speed_up_diarization_speed_for_whisperx/ | false | false | self | 2 | {'enabled': False, 'images': [{'id': 'p9RdTnpFx8NSXcGixTUTZG0UdqP6-VuBYDzcMRnYEvw', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/p9RdTnpFx8NSXcGixTUTZG0UdqP6-VuBYDzcMRnYEvw.png?width=108&crop=smart&auto=webp&s=f4bd2fc76f712c24725a8a75486eda20e526bbb8', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/p9RdTnpFx8NSXcGixTUTZG0UdqP6-VuBYDzcMRnYEvw.png?width=216&crop=smart&auto=webp&s=89ced2ee67f1ff3232cb8bc6fbd078d085b3ce28', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/p9RdTnpFx8NSXcGixTUTZG0UdqP6-VuBYDzcMRnYEvw.png?width=320&crop=smart&auto=webp&s=432e56095aec2a1edd1748d92993c7081d407e7e', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/p9RdTnpFx8NSXcGixTUTZG0UdqP6-VuBYDzcMRnYEvw.png?width=640&crop=smart&auto=webp&s=e7fc64f98a4ac05c5e32884fe58f510c4f4becc9', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/p9RdTnpFx8NSXcGixTUTZG0UdqP6-VuBYDzcMRnYEvw.png?width=960&crop=smart&auto=webp&s=62f3922e4ee56a59a8fa49df020c0f1107944874', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/p9RdTnpFx8NSXcGixTUTZG0UdqP6-VuBYDzcMRnYEvw.png?width=1080&crop=smart&auto=webp&s=0f65416f643550444941defd3d2ce1903f8c1bb2', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/p9RdTnpFx8NSXcGixTUTZG0UdqP6-VuBYDzcMRnYEvw.png?auto=webp&s=fdcc260b2030dab53320e4103d2d38392b427411', 'width': 1200}, 'variants': {}}]} |
how to choose a model | 1 | hey i m new to local LLM i m using n8n and i m trying to find the best model for me i have this :
OS: Ubuntu 24.04.3 LTS x86\_64
Kernel: 6.8.0-87-generic
CPU: AMD FX-8300 (8) @ 3.300GHz
GPU: NVIDIA GeForce GTX 1060 3GB
Memory: 4637MiB / 15975MiB
which AI model is the best for me ? i tryed phi3 and gemma3 on ollama do you think i can run a larger model ? | 2025-11-04T12:58:23 | https://www.reddit.com/r/LocalLLaMA/comments/1oo6dq6/how_to_choose_a_model/ | nobody-was-there | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oo6dq6 | false | null | t3_1oo6dq6 | /r/LocalLLaMA/comments/1oo6dq6/how_to_choose_a_model/ | false | false | self | 1 | null |
KTransformers Open Source New Era: Local Fine-tuning of Kimi K2 and DeepSeek V3 | 30 | ERROR: type should be string, got "https://preview.redd.it/hvu5ohojj8zf1.png?width=1440&format=png&auto=webp&s=45b8043885b8171ead6c7ecef513f2585f53e186\n\nKTransformers has enabled multi-GPU inference and local fine-tuning capabilities through collaboration with the SGLang and LLaMa-Factory communities. Users can now support higher-concurrency local inference via multi-GPU parallelism and fine-tune ultra-large models like DeepSeek 671B and Kimi K2 1TB locally, greatly expanding the scope of applications.\n\nA dedicated introduction to the Expert Deferral feature just [submitted](https://github.com/sgl-project/sglang/pull/12586) to the SGLang\n\nIn short, our original CPU/GPU parallel scheme left the CPU idle during MLA computation—already a bottleneck—because it only handled routed experts, forcing CPU and GPU to run alternately, which was wasteful.\n\nhttps://preview.redd.it/7h4zt7hyj8zf1.png?width=1637&format=png&auto=webp&s=986fec35461ecfb03ead784b800459235017566b\n\nOur fix is simple: leveraging the residual network property, we defer the accumulation of the least-important few (typically 4) of the top-k experts to the next layer’s residual path. This effectively creates a parallel attn/ffn structure that increases CPU/GPU overlap.\n\nExperiments (detailed numbers in our SOSP’25 [paper](https://madsys.cs.tsinghua.edu.cn/publication/ktransformers-unleashing-the-full-potential-of-cpu/gpu-hybrid-inference-for-moe-models/SOSP25-chen.pdf)) show that deferring, rather than simply skipping, largely preserves model quality while boosting performance by over 30%. Such system/algorithm co-design is now a crucial optimization avenue, and we are exploring further possibilities.\n\n# Fine-tuning with LLaMA-Factory \n\nCompared to the still-affordable API-based inference, local fine-tuning—especially light local fine-tuning after minor model tweaks—may in fact be a more important need for the vast community of local players. After months of development and tens of thousands of lines of code, this feature has finally been implemented and open-sourced today with the help of the LLaMA-Factory community.\n\nhttps://preview.redd.it/o3nes3sbk8zf1.png?width=1440&format=png&auto=webp&s=84f080c9ebfa1b3202001242174549236bc8f83d\n\nSimilar to Unsloth’s GPU memory-reduction capability, LLaMa-Factory integrated with KTransformers can, when VRAM is still insufficient, leverage CPU/AMX-instruction compute for CPU-GPU heterogeneous fine-tuning, achieving the dramatic drop in VRAM demand shown below. With just one server plus two RTX 4090s, you can now fine-tune DeepSeek 671B locally!\n\nhttps://preview.redd.it/u7yqc13fk8zf1.png?width=1136&format=png&auto=webp&s=5ede3dcf8eb95134929b452b67bc76dfeb8e8730\n\n" | 2025-11-04T12:44:46 | https://www.reddit.com/r/LocalLLaMA/comments/1oo62ww/ktransformers_open_source_new_era_local/ | nekofneko | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oo62ww | false | null | t3_1oo62ww | /r/LocalLLaMA/comments/1oo62ww/ktransformers_open_source_new_era_local/ | false | false | 30 | null | |
Disappointed by dgx spark | 563 | just tried Nvidia dgx spark irl
gorgeous golden glow, feels like gpu royalty
…but 128gb shared ram still underperform whenrunning qwen 30b with context on vllm
for 5k usd, 3090 still king if you value raw speed over design
anyway, wont replce my mac anytime soon | 2025-11-04T12:43:28 | RockstarVP | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1oo6226 | false | null | t3_1oo6226 | /r/LocalLLaMA/comments/1oo6226/disappointed_by_dgx_spark/ | false | false | 563 | {'enabled': True, 'images': [{'id': 'EK_tn-LvzmNS97IqACV-RawTOB5SMjomF4qO_R_p_0s', 'resolutions': [{'height': 81, 'url': 'https://preview.redd.it/a1tbzs1dk8zf1.jpeg?width=108&crop=smart&auto=webp&s=0d6fa72249d1bb1d96e3356d075d57ea81c219b8', 'width': 108}, {'height': 162, 'url': 'https://preview.redd.it/a1tbzs1dk8zf1.jpeg?width=216&crop=smart&auto=webp&s=99a01b157fb34247af422910ef4e1d04fd331074', 'width': 216}, {'height': 240, 'url': 'https://preview.redd.it/a1tbzs1dk8zf1.jpeg?width=320&crop=smart&auto=webp&s=33602527cf3ab64cdd51da75f2bc50361dbc91c0', 'width': 320}, {'height': 480, 'url': 'https://preview.redd.it/a1tbzs1dk8zf1.jpeg?width=640&crop=smart&auto=webp&s=090e0bdb3a3f9757ae6bdbff3964dc951a1361ed', 'width': 640}, {'height': 720, 'url': 'https://preview.redd.it/a1tbzs1dk8zf1.jpeg?width=960&crop=smart&auto=webp&s=9351770b66cebe076618b8b279117e99abc5a390', 'width': 960}, {'height': 810, 'url': 'https://preview.redd.it/a1tbzs1dk8zf1.jpeg?width=1080&crop=smart&auto=webp&s=9a3ce37a6b55a18162704e31d92a2ffffe4e6285', 'width': 1080}], 'source': {'height': 3024, 'url': 'https://preview.redd.it/a1tbzs1dk8zf1.jpeg?auto=webp&s=1d8287a212741a3875594c3810e919a2349b5f10', 'width': 4032}, 'variants': {}}]} | ||
Does blackwell/new GPU matter to train model with MXFP4 ? | 0 | Hi,
Does newer gpu ( like blackwell ) matter when you want to fine-tune/RL a model with MXFP4 quant like gpt-oss:20b ? | 2025-11-04T12:36:14 | https://www.reddit.com/r/LocalLLaMA/comments/1oo5z3w/does_blackwellnew_gpu_matter_to_train_model_with/ | vdiallonort | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oo5z3w | false | null | t3_1oo5z3w | /r/LocalLLaMA/comments/1oo5z3w/does_blackwellnew_gpu_matter_to_train_model_with/ | false | false | self | 0 | null |
Built a lightweight RAG management tool that only reprocesses what actually changed. | 7 | I built a small tool that lets you edit your RAG data efficiently
So, during my internship I worked on a few RAG setups and one thing that always slowed us down was to them. Every small change in the documents made us reprocessing and reindexing everything from the start.
Recently, I have started working on optim-rag on a goal to reduce this overhead. Basically, It lets you open your data, edit or delete chunks, add new ones, and only reprocesses what actually changed when you commit those changes.
I have been testing it on my own textual notes and research material and updating stuff has been a lot a easier for me at least.
repo → [github.com/Oqura-ai/optim-rag](http://github.com/Oqura-ai/optim-rag)
This project is still in its early stages, and there’s plenty I want to improve. But since it’s already at a usable point as a primary application, I decided not to wait and just put it out there. Next, I’m planning to make it DB agnostic as currently it only supports qdrant.
I’m also planning to add local model support to all of my active projects, including this one. The main challenge right now is doing this on a student budget, I’ve only got a **4GB RTX 3050 + 16GB RAM** on my laptop. If anyone has experience in building tools with local model supports efficiently or tips on testing quality with limited VRAM, I’d really appreciate your suggestions. | 2025-11-04T12:35:53 | https://www.reddit.com/r/LocalLLaMA/comments/1oo5yz6/built_a_lightweight_rag_management_tool_that_only/ | Interesting-Area6418 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oo5yz6 | false | null | t3_1oo5yz6 | /r/LocalLLaMA/comments/1oo5yz6/built_a_lightweight_rag_management_tool_that_only/ | false | false | self | 7 | {'enabled': False, 'images': [{'id': '6ENBJ_F33jpef7a84dqus9KiuwT9h4jb611VS3efIIg', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/6ENBJ_F33jpef7a84dqus9KiuwT9h4jb611VS3efIIg.png?width=108&crop=smart&auto=webp&s=1d70057c1eedb8f3b009bfad1b4aed7137829506', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/6ENBJ_F33jpef7a84dqus9KiuwT9h4jb611VS3efIIg.png?width=216&crop=smart&auto=webp&s=1a86906153bc642ba95d786a462a794bc51c08aa', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/6ENBJ_F33jpef7a84dqus9KiuwT9h4jb611VS3efIIg.png?width=320&crop=smart&auto=webp&s=a3a33453129db29129024b98bc4984bef7c59dc2', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/6ENBJ_F33jpef7a84dqus9KiuwT9h4jb611VS3efIIg.png?width=640&crop=smart&auto=webp&s=cb499e4202d68666c713c19bfffab2bf6106dad7', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/6ENBJ_F33jpef7a84dqus9KiuwT9h4jb611VS3efIIg.png?width=960&crop=smart&auto=webp&s=b425d444289f5636e7f8b5884fcafc4ecdb077c1', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/6ENBJ_F33jpef7a84dqus9KiuwT9h4jb611VS3efIIg.png?width=1080&crop=smart&auto=webp&s=ab1f34cfb60549d92d12b24ec492c8d7476978d4', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/6ENBJ_F33jpef7a84dqus9KiuwT9h4jb611VS3efIIg.png?auto=webp&s=af7014ea6af9a9b1ae9c70d3c55195ef64a9787d', 'width': 1200}, 'variants': {}}]} |
Minimax M2 Support MCP, Images | 3 | I've been testing for the last week across Kilocode and Claude CLI the performance is outstanding. For now it's optimized toward CC
Kilo we get considerable drop in performance and keep rate limit
I'm hoping with M2.1 they release multimodal so far it doesn't support Images or MCP that's a bummer | 2025-11-04T12:15:56 | https://www.reddit.com/r/LocalLLaMA/comments/1oo5reb/minimax_m2_support_mcp_images/ | zakblacki | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oo5reb | false | null | t3_1oo5reb | /r/LocalLLaMA/comments/1oo5reb/minimax_m2_support_mcp_images/ | false | false | self | 3 | null |
Seeking advice for a small model ro run on my laptop | 3 | Hey I wanna prompt questions and get answers for video automation reasons
Specs:
16GB RAM
Intel Core i7-12650h (16CPUS) 2.3GhHz
Nvidia GeForce RTX 4060 Laptop GPU (8GBVRAM)
1TB SSD
| 2025-11-04T11:50:57 | https://www.reddit.com/r/LocalLLaMA/comments/1oo5dbf/seeking_advice_for_a_small_model_ro_run_on_my/ | Drakooon05 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oo5dbf | false | null | t3_1oo5dbf | /r/LocalLLaMA/comments/1oo5dbf/seeking_advice_for_a_small_model_ro_run_on_my/ | false | false | self | 3 | null |
Dual 5090 work station for SDXL | 2 | **TL;DR:**
Building a small AI workstation with 2× RTX 5090 for SDXL, light video generation, and occasional LLM inference (7B–13B). Testing hot inference on-prem to reduce AWS costs. Open to GPU suggestions, including older big‑VRAM cards (AMD MI50 / MI100, older NVIDIA datacenter) for offline large batch work. Budget-conscious, want best value/performance mix.
Hey Guys,
I’ve a startup and currently using L40’s in AWS but there are times when we have no traffic and the boot time is terrible. I decided to build a small AI workstation as a POC to handle the lower traffic and costs to keep the models hot — which later I’ll take the cards out and put into a server rack on site.
I bought 2 x 5090’s, 128 GB DDR5 6400 CL40 and running on a spare 13700K + Asus Prime Z790‑P I never used.
I researched the numbers, render times, watts cost etc and besides having only 32 GB VRAM the cards seem they will run fast fine with CUDA parallelism and doing small batch processing. My models will fit. I spent about €2040 (ex VAT) per MSI Gaming Trio and just got them delivered. Just doubting if I made the best choice on cards, 4090s are near the same price in Europe, 3090s hard to get. I was planning to buy 8 5090s and put them together due to running smaller models and keep training in the cloud if this POC works out.
This is just a temporary test setup — it will all be put into a server eventually. I can add 2 more cards into the motherboard. Models mostly fit in memory, so PCIe bandwidth loss is not a big issue. I’m also looking to do **offline large batch work**, so older cards could take longer to process but may still be cost‑effective.
**Workloads & Use‑cases:**
* SDXL (text‑to‑image)
* Soon: video generation (likely small batches initially)
* Occasional LLM inference (probably 7B–13B parameter models)
* MCP server
**Questions I’m wrestling with:**
* Better GPU choices?
* For inference‑heavy workloads (image + video + smaller LLMs), are there better value workstation or data center cards I should consider?
* Would AMD MI50 / MI100, or older NVIDIA data‑center cards (A100, H100) be better for occasional LLM inference due to higher VRAM, even if slightly slower for image/video tasks?
* I’m mostly looking for advice on value and performance for inference, especially for SDXL, video generation, and small LLM inference. Budget is limited, but I want to do as much as possible on‑prem.
* I’m **open to any card suggestions or best-value hacks** :)
Thanks in advance for any insights! | 2025-11-04T11:42:55 | https://www.reddit.com/r/LocalLLaMA/comments/1oo5862/dual_5090_work_station_for_sdxl/ | Background-Bank1798 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oo5862 | false | null | t3_1oo5862 | /r/LocalLLaMA/comments/1oo5862/dual_5090_work_station_for_sdxl/ | false | false | self | 2 | null |
Schema based prompting | 30 | I'd argue using json schemas for inputs/outputs makes model interactions more reliable, especially when working on agents across different models. Mega prompts that cover all edge cases work with only one specific model. New models get released on a weekly or existing ones get updated, then older versions are discontinued and you have to start over with your prompt.
Why isn't schema based prompting more common practice? | 2025-11-04T11:18:09 | https://www.reddit.com/r/LocalLLaMA/comments/1oo4sfz/schema_based_prompting/ | facethef | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oo4sfz | false | null | t3_1oo4sfz | /r/LocalLLaMA/comments/1oo4sfz/schema_based_prompting/ | false | false | self | 30 | null |
What's the biggest most common PROBLEM you have in your personal ML/AI side projects? | 7 | Hey there, I'm currently trying to start my first SaaS and I'm searching for a genuinly painful problem to create a solution. Need your help. Got a quick minute to help me?
I'm specifically interested in things that are taking your time, money, or effort. Would be great if you tell me the story. | 2025-11-04T11:07:02 | https://www.reddit.com/r/LocalLLaMA/comments/1oo4lhs/whats_the_biggest_most_common_problem_you_have_in/ | HectorAlcazar11 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oo4lhs | false | null | t3_1oo4lhs | /r/LocalLLaMA/comments/1oo4lhs/whats_the_biggest_most_common_problem_you_have_in/ | false | false | self | 7 | null |
Finetuning DeepSeek 671B locally with only 80GB VRAM and Server CPU | 101 | Hi, we're the KTransformers team (formerly known for our DeepSeek-V3 local CPU/GPU hybrid inference project).
Today, we're proud to announce full integration with LLaMA-Factory, enabling you to **fine-tune DeepSeek-671B or Kimi-K2-1TB locally with just 4x RTX 4090 GPUs**!
https://preview.redd.it/dlipq1us28zf1.png?width=2332&format=png&auto=webp&s=92fad09b19f37c76c3f08fe9e326816ad4d533d1
[](https://preview.redd.it/finetuning-deepseek-671b-locally-with-only-80gb-vram-and-v0-24938oydy7zf1.png?width=2246&format=png&auto=webp&s=216765e8119e54cc2bdc92bf24b082575f7d1bdc)
[](https://preview.redd.it/finetuning-deepseek-671b-locally-with-only-80gb-vram-and-v0-w1m1j89jy7zf1.png?width=2570&format=png&auto=webp&s=0bde4b33c857b8fd4c1f4d8c0c4ecc42763f5bbc)
More infomation can be found at
[https://github.com/kvcache-ai/ktransformers/tree/main/KT-SFT](https://github.com/kvcache-ai/ktransformers/tree/main/KT-SFT) | 2025-11-04T11:05:26 | https://www.reddit.com/r/LocalLLaMA/comments/1oo4kh7/finetuning_deepseek_671b_locally_with_only_80gb/ | CombinationNo780 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1oo4kh7 | false | null | t3_1oo4kh7 | /r/LocalLLaMA/comments/1oo4kh7/finetuning_deepseek_671b_locally_with_only_80gb/ | false | false | 101 | null |
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