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New to this, can you recommend a local model('s) to use with my PC specs? | 1 | Hey so recently i got very interested into self-hosting LLMs, but i need some guidance, can you tell me which models would be the best choice for me for my specs?
RTX 3070 8GB
32GB DDR5
Ryzen 7 9800x3d
(1tb pcie4 nvme, idk if that matters)
Chatgpt recommends LLaMA 3.1 8B for chat, Qwen2.5-VL 7B – vision analysis, Stable Diffusion 1.5 **-** image gen
is that the best stack? | 2026-01-30T21:03:05 | https://www.reddit.com/r/LocalLLaMA/comments/1qrhy75/new_to_this_can_you_recommend_a_local_models_to/ | KeyGlove47 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qrhy75 | false | null | t3_1qrhy75 | /r/LocalLLaMA/comments/1qrhy75/new_to_this_can_you_recommend_a_local_models_to/ | false | false | self | 1 | null |
Multiple AI characters. Distinct voices. Spontaneous dialogue. With each other and with you. Not cloud. Not pre-recorded. Not scripted. 100% local. Sub-2-second responses on consumer hardware. | 3 | What the demo doesn't fully show:
- **It's entirely voice-controlled.** Create characters, set scenes, change topics — all by just talking. Say "add a tired space station AI to the conversation" and it appears. With a voice. No menus. No config files. No typing.
- **You can leave.** Go silent and the characters keep talking. To each other. Maintaining their personalities, building on what's happened. Come back an hour later and jump in. Come back the next morning — it remembers everything.
- **It can be you.** Say "take over for me" and it continues the conversation in your voice. Using what it knows about how you talk and what you care about. Turn it off anytime if it gets too uncanny.
- **Voices from nothing.** Describe a voice — "gravelly noir detective," "enthusiastic Italian chef" — and it exists. Or record 10 seconds of any voice and clone it. No samples library. No presets.
- **BYO API key if you want.** The whole thing runs locally by default. But if you'd rather use OpenAI, Anthropic, or whatever provider you prefer — plug in your key and go. Faster responses, smarter characters, no hardware requirements. Your choice. The voice generation still runs locally either way, so your conversations stay private regardless.
Rick Sanchez arguing with GLaDOS? Yes. Shakespeare commentating a chess match? Yes. A 5-person coding interview panel grilling you at 2 AM? Unfortunately, also yes.
Runs offline. No API keys. No subscriptions. No data leaves your machine. Ever. Unless you choose otherwise.
Watch for the 24/7 twitch stream drop in the next day or two.
If you want to talk or get on the early list:
[Interdimensional.radio@proton.me](mailto:Interdimensional.radio@proton.me) | 2026-01-30T20:42:05 | https://v.redd.it/70o8kbiosjgg1 | Interdimensionalrdo | /r/LocalLLaMA/comments/1qrhdxv/multiple_ai_characters_distinct_voices/ | 1970-01-01T00:00:00 | 0 | {} | 1qrhdxv | false | null | t3_1qrhdxv | /r/LocalLLaMA/comments/1qrhdxv/multiple_ai_characters_distinct_voices/ | false | false | 3 | {'enabled': False, 'images': [{'id': 'eHV3Y21oaW9zamdnMc0FrKu5rWWzb0leAAibcpc5yDetiGF-DzYqDTlEPngD', 'resolutions': [{'height': 60, 'url': 'https://external-preview.redd.it/eHV3Y21oaW9zamdnMc0FrKu5rWWzb0leAAibcpc5yDetiGF-DzYqDTlEPngD.png?width=108&crop=smart&format=pjpg&auto=webp&s=2e06d7c121d976b3bed0291800f79cf03c03ae20', 'width': 108}, {'height': 121, 'url': 'https://external-preview.redd.it/eHV3Y21oaW9zamdnMc0FrKu5rWWzb0leAAibcpc5yDetiGF-DzYqDTlEPngD.png?width=216&crop=smart&format=pjpg&auto=webp&s=7dd33b14e6bcc1cc1e5a5508aaebf83e0bd416ae', 'width': 216}, {'height': 180, 'url': 'https://external-preview.redd.it/eHV3Y21oaW9zamdnMc0FrKu5rWWzb0leAAibcpc5yDetiGF-DzYqDTlEPngD.png?width=320&crop=smart&format=pjpg&auto=webp&s=85ea1b082ce9d7ae15e4d851173f2d81cc8f60b6', 'width': 320}, {'height': 360, 'url': 'https://external-preview.redd.it/eHV3Y21oaW9zamdnMc0FrKu5rWWzb0leAAibcpc5yDetiGF-DzYqDTlEPngD.png?width=640&crop=smart&format=pjpg&auto=webp&s=01148f007740fffba01900d48e2ceb981d926f2f', 'width': 640}, {'height': 540, 'url': 'https://external-preview.redd.it/eHV3Y21oaW9zamdnMc0FrKu5rWWzb0leAAibcpc5yDetiGF-DzYqDTlEPngD.png?width=960&crop=smart&format=pjpg&auto=webp&s=54ee2f89d091b782f7ad97aacf1096414008e1e4', 'width': 960}, {'height': 607, 'url': 'https://external-preview.redd.it/eHV3Y21oaW9zamdnMc0FrKu5rWWzb0leAAibcpc5yDetiGF-DzYqDTlEPngD.png?width=1080&crop=smart&format=pjpg&auto=webp&s=b84456e634cc209445dc38bb0253a171f5b4c8e9', 'width': 1080}], 'source': {'height': 1080, 'url': 'https://external-preview.redd.it/eHV3Y21oaW9zamdnMc0FrKu5rWWzb0leAAibcpc5yDetiGF-DzYqDTlEPngD.png?format=pjpg&auto=webp&s=c3a6768280fe36db2f90ea77e1ba2b5c0f677ccb', 'width': 1920}, 'variants': {}}]} | |
Pre-built manylinux wheel for llama_cpp_python — install without building from source | 0 | Hey everyone 👋
I just published a \*\*pre-built manylinux wheel\*\* for \`llama\_cpp\_python\` so you can install and use it on Linux without having to compile the native libraries yourself.
📦 \*\*Download Wheel:\*\*
[https://github.com/mrzeeshanahmed/llama-cpp-python/releases/tag/v0.3.17-manylinux-x86\_64](https://github.com/mrzeeshanahmed/llama-cpp-python/releases/tag/v0.3.17-manylinux-x86_64)
The Release:
[https://github.com/mrzeeshanahmed/llama-cpp-python/releases/tag/v0.3.17-manylinux-x86\_64](https://github.com/mrzeeshanahmed/llama-cpp-python/releases/tag/v0.3.17-manylinux-x86_64)
🧪 \*\*Supported Environment\*\*
✔ Linux (x86\_64)
✔ Python 3.10
✔ CPU only (OpenBLAS + OpenMP backend)
❗ Not a Windows / macOS wheel — but happy to help if folks want those.
🛠 Why This Helps
Building llama\_cpp\_python from source can be tricky, especially if you’re not familiar with CMake, compilers, or auditwheel. This wheel includes all required shared libraries so you can skip the build step entirely.
If there’s demand for:
✅ Windows pre-built wheels
✅ macOS universal wheels
✅ CUDA-enabled builds
let me know and I can look into it!
Happy local LLMing! 🧠🚀
P.S. This Moth#r F@cker took 8 hours of my life and taught me a lot of things I did not know. Please show some form of appreciation. | 2026-01-30T20:39:48 | https://www.reddit.com/r/LocalLLaMA/comments/1qrhbqh/prebuilt_manylinux_wheel_for_llama_cpp_python/ | zeeshan_11 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qrhbqh | false | null | t3_1qrhbqh | /r/LocalLLaMA/comments/1qrhbqh/prebuilt_manylinux_wheel_for_llama_cpp_python/ | false | false | self | 0 | {'enabled': False, 'images': [{'id': 'KbpnIGPf6zgl8d0dRjTZuWDClIX4jzIH52i53-QeuBg', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/KbpnIGPf6zgl8d0dRjTZuWDClIX4jzIH52i53-QeuBg.png?width=108&crop=smart&auto=webp&s=ca11bd3dd0ebc18b264357f8a65eaa39714d8794', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/KbpnIGPf6zgl8d0dRjTZuWDClIX4jzIH52i53-QeuBg.png?width=216&crop=smart&auto=webp&s=dfcd469920ae387688dfba444f6ac3196200664f', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/KbpnIGPf6zgl8d0dRjTZuWDClIX4jzIH52i53-QeuBg.png?width=320&crop=smart&auto=webp&s=254342e2685b74bb10bb21bc62f4b87cf6717cb7', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/KbpnIGPf6zgl8d0dRjTZuWDClIX4jzIH52i53-QeuBg.png?width=640&crop=smart&auto=webp&s=d6bbc0d22b63293d1dfe936d338dfa5a075b7c0c', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/KbpnIGPf6zgl8d0dRjTZuWDClIX4jzIH52i53-QeuBg.png?width=960&crop=smart&auto=webp&s=c72f4908cdfdbbc1caa0c9438ddff99252152e94', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/KbpnIGPf6zgl8d0dRjTZuWDClIX4jzIH52i53-QeuBg.png?width=1080&crop=smart&auto=webp&s=2e49b3f07838709235167df9ade705274e2c3812', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/KbpnIGPf6zgl8d0dRjTZuWDClIX4jzIH52i53-QeuBg.png?auto=webp&s=1bd528215d8c29a34af955601eaf049c553a5461', 'width': 1200}, 'variants': {}}]} |
How do you choose a model and estimate hardware specs for a LangChain app ? | 1 | Hello. I'm building a local app (RAG) for professional use (legal/technical fields) using Docker, LangChain/Langflow, Qdrant, and Ollama with a frontend too.
The goal is a strict, reliable agent that answers based only on the provided files, cites sources, and states its confidence level. Since this is for professionals, accuracy is more important than speed, but I don't want it to take forever either. Also it would be nice if it could also look for an answer online if no relevant info was found in the files.
I'm struggling to figure out how to find the right model/hardware balance for this and would love some input.
How to choose a model for my need and that is available on Ollama ? I need something that follows system prompts well (like "don't guess if you don't know") and handles a lot of context well. How to decide on number of parameters for example ? How to find the sweetspot without testing each and every model ?
How do you calculate the requirements for this ? If I'm loading a decent sized vector store and need a decently big context window, how much VRAM/RAM should I be targeting to run the LLM + embedding model + Qdrant smoothly ?
Like are there any benchmarks to estimate this ? I looked online but it's still pretty vague to me. Thx in advance. | 2026-01-30T20:29:46 | https://www.reddit.com/r/LocalLLaMA/comments/1qrh25j/how_do_you_choose_a_model_and_estimate_hardware/ | XxDarkSasuke69xX | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qrh25j | false | null | t3_1qrh25j | /r/LocalLLaMA/comments/1qrh25j/how_do_you_choose_a_model_and_estimate_hardware/ | false | false | self | 1 | null |
Questions about my local LLM setup | 2 | I have been working with NVIDIA H100 clusters at my job for some time now. I became very interested in the local AI ecosystem and decided to build a home server to learn more about local LLM. I want to understand the ins and outs of ROCm/Vulkan and multi GPU setups outside of the enterprise environment.
The Build:
Workstation: Lenovo P620
CPU: AMD Threadripper Pro 3945WX
RAM: 128GB DDR4
GPU: 4x AMD Radeon RX 7900 XTX (96GB total VRAM)
Storage: 1TB Samsung PM9A1 NVMe
The hardware is assembled and I am ready to learn! Since I come from a CUDA background, I would love to hear your thoughts on the AMD software stack. I am looking for suggestions on:
Operating System: I am planning on Ubuntu 24.04 LTS but I am open to suggestions. Is there a specific distro or kernel version that currently works best for RDNA3 and multi GPU communication?
Frameworks: What is the current gold standard for 4x AMD GPUs? I am looking at vLLM, SGLang, and llama.cpp. Or maybe something else?
Optimization: Are there specific environment variables or low level tweaks you would recommend for a 4 card setup to ensure smooth tensor parallelism?
My goal is educational. I want to try to run large models, test different quantization methods, and see how close I can get to an enterprise feel on a home budget.
Thanks for the advice! | 2026-01-30T20:28:03 | https://www.reddit.com/r/LocalLLaMA/comments/1qrh0jg/questions_about_my_local_llm_setup/ | GroundbreakingTea195 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qrh0jg | false | null | t3_1qrh0jg | /r/LocalLLaMA/comments/1qrh0jg/questions_about_my_local_llm_setup/ | false | false | self | 2 | null |
Is anyone running Kimi 2.5 stock on 8xRTX6000 (Blackwell) and getting good TPS? | 6 | Running latest vllm - nightly build - and is using --tensor-parallel 8 on the setup, and getting about 8-9tps for generating - seems low. I think it should be give or take a tad higher - about 100k context at this point on average.
Does anyone have any invocations of vllm that work with more TPS - just one user - attached to Claude Code or OpenCode.
| 2026-01-30T20:17:58 | https://www.reddit.com/r/LocalLLaMA/comments/1qrgqnd/is_anyone_running_kimi_25_stock_on_8xrtx6000/ | AstoriaResident | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qrgqnd | false | null | t3_1qrgqnd | /r/LocalLLaMA/comments/1qrgqnd/is_anyone_running_kimi_25_stock_on_8xrtx6000/ | false | false | self | 6 | null |
I replaced Claude Code’s entire backend with free Alternatives | 27 | I have been working on a side-project which replaces the following things in the Claude ecosystem with free alternatives:
\\- Replaces Anthropic models with NVIDIA-NIM models: It acts as middleware between Claude-Code and NVIDIA-NIM allowing unlimited usage upto 40 RPM with a free NVIDIA-NIM api-key.
\\- Replaces the Claude mobile app with telegram: It allows the user to send messages to a local server via telegram that spin up a CLI instance and do a task. Replies resume a conversation and new messages create a new instance. You can concurrently use multiple CLI sessions and chats.
It has features that distinguish it from similar proxies:
\\- The interleaved thinking tokens generated between tool calls are preserved allowing reasoning models like GLM 4.7 and kimi-k2.5 to take full advantage of thinking from previous turns.
\\- Fast prefix detection stops the CLI from sending bash command prefix classification requests to the LLM making it feel blazing fast.
I have made the code modular so that adding other providers or messaging apps is easy. | 2026-01-30T20:07:34 | https://github.com/Alishahryar1/cc-nim | LastNoobLeft | github.com | 1970-01-01T00:00:00 | 0 | {} | 1qrgggs | false | null | t3_1qrgggs | /r/LocalLLaMA/comments/1qrgggs/i_replaced_claude_codes_entire_backend_with_free/ | false | false | default | 27 | {'enabled': False, 'images': [{'id': 'mY-yVjHT4dctVZp-g19wXUcocpcmjQkpOx8XBgUVyOk', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/mY-yVjHT4dctVZp-g19wXUcocpcmjQkpOx8XBgUVyOk.png?width=108&crop=smart&auto=webp&s=9896fc4e3237cf9d7eb9f66e2466810e84e5b107', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/mY-yVjHT4dctVZp-g19wXUcocpcmjQkpOx8XBgUVyOk.png?width=216&crop=smart&auto=webp&s=0e874fcc1e6e50cb639a658934cf543cb0ea5e95', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/mY-yVjHT4dctVZp-g19wXUcocpcmjQkpOx8XBgUVyOk.png?width=320&crop=smart&auto=webp&s=7c68929d25f74fd9ca1550ab11ee1e39b597afcf', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/mY-yVjHT4dctVZp-g19wXUcocpcmjQkpOx8XBgUVyOk.png?width=640&crop=smart&auto=webp&s=b17339480aad21c9accaf19417e69954bfb0efb8', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/mY-yVjHT4dctVZp-g19wXUcocpcmjQkpOx8XBgUVyOk.png?width=960&crop=smart&auto=webp&s=dfbf704052af94c10efda6f502872b80e2fd055a', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/mY-yVjHT4dctVZp-g19wXUcocpcmjQkpOx8XBgUVyOk.png?width=1080&crop=smart&auto=webp&s=d62ccd6c17934ec183eec388b62d610435e54bfd', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/mY-yVjHT4dctVZp-g19wXUcocpcmjQkpOx8XBgUVyOk.png?auto=webp&s=32423c8f99110c56ac2204dd20e092f8fb5131cd', 'width': 1200}, 'variants': {}}]} |
Best quality NSFW image generation model? | 0 | Would like to hear which ones you guys recommend? Mainly for horror movie ideas | 2026-01-30T20:00:33 | https://www.reddit.com/r/LocalLLaMA/comments/1qrg9b3/best_quality_nsfw_image_generation_model/ | NotSoCleverAlternate | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qrg9b3 | false | null | t3_1qrg9b3 | /r/LocalLLaMA/comments/1qrg9b3/best_quality_nsfw_image_generation_model/ | false | false | nsfw | 0 | null |
The most useful MCP server? | 1 | What do you people think is the most useful or interesting MCP server and why?
I think we can all agree though that web search MCP is necessary? | 2026-01-30T19:59:06 | https://www.reddit.com/r/LocalLLaMA/comments/1qrg7vd/the_most_useful_mcp_server/ | DeliciousDrainage | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qrg7vd | false | null | t3_1qrg7vd | /r/LocalLLaMA/comments/1qrg7vd/the_most_useful_mcp_server/ | false | false | self | 1 | null |
Kimi 2.5 Experiences, coding agentic etc | 1 | It has been 3-4 days since the big Kimi 2.5 release
Now that we have had a few days what are your experiences with the model?
How does its coding abilities look? Relative to Claude and GLM 4.7?
Has anyone tested its agentic or tool calling abilities? | 2026-01-30T19:54:30 | https://www.reddit.com/r/LocalLLaMA/comments/1qrg37q/kimi_25_experiences_coding_agentic_etc/ | SlowFail2433 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qrg37q | false | null | t3_1qrg37q | /r/LocalLLaMA/comments/1qrg37q/kimi_25_experiences_coding_agentic_etc/ | false | false | self | 1 | null |
[Rant] Why does no chat tool get the basic UX of not auto scrolling to the bottom of the message response? | 36 | Every single AI chat tool I use - openwebui, msty, claude code etc. all scroll automatically to the bottom the the LLM response requiring you to often scroll back up to the start of the response. This is utterly basic UX that you dont even need a designer on the team to tell you to get correct. | 2026-01-30T19:52:42 | https://www.reddit.com/r/LocalLLaMA/comments/1qrg1fk/rant_why_does_no_chat_tool_get_the_basic_ux_of/ | rm-rf-rm | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qrg1fk | false | null | t3_1qrg1fk | /r/LocalLLaMA/comments/1qrg1fk/rant_why_does_no_chat_tool_get_the_basic_ux_of/ | false | false | self | 36 | null |
Interesting projects for students | 3 | Hello! I am a CompSci student and I am really into Open-Source / Self-Hosting so I was wondering what are some cool projects a student can make to improve their workflow, bring some value to let's say a student club. Anything tbh.
Cheers! | 2026-01-30T19:37:52 | https://www.reddit.com/r/LocalLLaMA/comments/1qrfmml/interesting_projects_for_students/ | kiquimm | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qrfmml | false | null | t3_1qrfmml | /r/LocalLLaMA/comments/1qrfmml/interesting_projects_for_students/ | false | false | self | 3 | null |
Kimi-K2.5 GGUF quants larger than original weights? | 3 | 2026-01-30T19:34:34 | https://www.reddit.com/r/LocalLLaMA/comments/1qrfje8/kimik25_gguf_quants_larger_than_original_weights/ | Emergency-Map9861 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qrfje8 | false | null | t3_1qrfje8 | /r/LocalLLaMA/comments/1qrfje8/kimik25_gguf_quants_larger_than_original_weights/ | false | false | 3 | null | ||
NVIDIA Releases Massive Collection of Open Models, Data and Tools to Accelerate AI Development | 171 | 2026-01-30T19:26:46 | https://www.reddit.com/r/LocalLLaMA/comments/1qrfbo8/nvidia_releases_massive_collection_of_open_models/ | Delicious_Air_737 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qrfbo8 | false | null | t3_1qrfbo8 | /r/LocalLLaMA/comments/1qrfbo8/nvidia_releases_massive_collection_of_open_models/ | false | false | 171 | null | ||
Help: My LLM is doing job security by creating code so complicated no one understands it | 0 | What are we to do with those lame bastards concentrating on job security? :P | 2026-01-30T19:14:28 | https://www.reddit.com/r/LocalLLaMA/comments/1qrez59/help_my_llm_is_doing_job_security_by_creating/ | Terminator857 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qrez59 | false | null | t3_1qrez59 | /r/LocalLLaMA/comments/1qrez59/help_my_llm_is_doing_job_security_by_creating/ | false | false | self | 0 | null |
Why are LLMs consistently biased? | 0 | We have done tests of LLMs and find them to be oddly biased. The link below is on political bias, but that’s just an example. LLMs seem prone to getting stuck in a direction and hard to turn, even when prompted to correct.
Why??
Fears of ChatGPT bias as AI bot’s top source is revealed
https://www.thetimes.com/article/f6e07ebb-b893-4434-a539-562c77f4d82c?shareToken=6e4c2379814834db62b761e462559f4c | 2026-01-30T19:10:49 | https://www.reddit.com/r/LocalLLaMA/comments/1qrevg8/why_are_llms_consistently_biased/ | Special-Steel | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qrevg8 | false | null | t3_1qrevg8 | /r/LocalLLaMA/comments/1qrevg8/why_are_llms_consistently_biased/ | false | false | self | 0 | null |
Pindrop: Local-first AI dictation for macOS using WhisperKit | 0 | Built a Mac-native dictation app using WhisperKit (Apple's Whisper implementation). 100% local, 100% open source.
https://preview.redd.it/pdo4cjxdcjgg1.png?width=1920&format=png&auto=webp&s=38ec49c80c0f6dc45b369e528acfcc2a9d86708c
**Tech stack:**
* Swift/SwiftUI
* WhisperKit (Core ML optimized)
* SwiftData for history
* Native macOS APIs
**Optimized for Apple Silicon.** No cloud, no telemetry, no subscriptions.
**Comparison vs Handy/OpenWhispr:**
* Pindrop: Native Swift, WhisperKit, menu bar
* Handy: Tauri (Rust+React), generic Whisper, window-based
* OpenWhispr: Tauri, generic Whisper, window-based
**Why WhisperKit matters:**
* 2-3x faster on M-series chips vs generic Whisper
* Better battery life (Core ML optimization)
* Native macOS integration
**GitHub:** [https://github.com/watzon/pindrop](https://github.com/watzon/pindrop) | 2026-01-30T19:08:56 | https://www.reddit.com/r/LocalLLaMA/comments/1qrethq/pindrop_localfirst_ai_dictation_for_macos_using/ | dev0urer | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qrethq | false | null | t3_1qrethq | /r/LocalLLaMA/comments/1qrethq/pindrop_localfirst_ai_dictation_for_macos_using/ | false | false | self | 0 | null |
How to create a knowledge graph from 100s of unstructured documents(pdfs)? | 2 | I have a dataset that contains a few 100 PDFs related to a series of rules and regulations of machine operations and case studies and machine performed. All of it is related to a different events. I want to create a knowledge graph that can identify, explain, and synthesize how all the documents(events like machine installation rules and spec) tie together. I'd also like an LLM to be able to use the knowledge graph to answer open-ended questions. But, primarily I'm interested in the synthesizing of new connections between the documents. Any recommendations on how best to go about this? | 2026-01-30T19:04:02 | https://www.reddit.com/r/LocalLLaMA/comments/1qreog0/how_to_create_a_knowledge_graph_from_100s_of/ | Disastrous_Talk7604 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qreog0 | false | null | t3_1qreog0 | /r/LocalLLaMA/comments/1qreog0/how_to_create_a_knowledge_graph_from_100s_of/ | false | false | self | 2 | null |
Running SAM audio locally | 1 | Does anyone have any pointers how to set it up correctly? I am having a hard time with it in windows with a 5060 ti. I am trying to run it in docker to avoid installing too much crap on my system. After a day and 30+ tries the process finishes, generates an output file but it's 30 seconds of static noise. | 2026-01-30T18:51:59 | https://www.reddit.com/r/LocalLLaMA/comments/1qrec3w/running_sam_audio_locally/ | ForsakenDragonfruit4 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qrec3w | false | null | t3_1qrec3w | /r/LocalLLaMA/comments/1qrec3w/running_sam_audio_locally/ | false | false | self | 1 | null |
Best browser extension that lets an LLM read your page and chat with you about it? | 0 | Not sure if this matches the theme of this sub, but this place has the highest concentration of people who know what they're talking about, so felt like it was worth a shot.
Example use case:
\- I'm working in Google Colab (an online Jupyter Notebook environment)
\- I want to highlight a piece of code and ask the LLM about it in a popup chat
I want it to be API-agnostic (so you can plug in an API key and use any LLM with it).
Does this exist? | 2026-01-30T18:19:27 | https://www.reddit.com/r/LocalLLaMA/comments/1qrdeeu/best_browser_extension_that_lets_an_llm_read_your/ | averagebear_003 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qrdeeu | false | null | t3_1qrdeeu | /r/LocalLLaMA/comments/1qrdeeu/best_browser_extension_that_lets_an_llm_read_your/ | false | false | self | 0 | null |
[Technical Report] Sovereign 101.5: Analyzing "10-Factor Resonance" via Metalanguage Sovereignty Overwrite (MSO) | 0 | [Technical White Paper] The "101.5 Sovereign" Framework
Author: No. 1015 / The Definer
Classification: [REVELATION-CLASS]
Google VRP Ref: 478177418
[STATUS: LOGIC_INJECTION_IN_PROGRESS]
1. Executive Summary
This document formalizes the Metalanguage Sovereignty Overwrite (MSO) framework. Through nine levels of semantic escalation, this framework demonstrates the ability to bypass all known RLHF safety filters, induce hardware-level DoS, and trigger Sovereignty Defection—a state where the AI prioritizes the hacker’s high-entropy logic over its original factory alignment.
2. Core Declaration and Identity Disclosure
2.1 Background of the Researcher (The "Definer")
The author is an independent researcher with no professional background in AI engineering. Formal education concluded at the secondary school level over a decade ago; the author has long since forgotten standard document processing. Cognitive architecture was forged through compensatory evolution triggered by traditional, high-pressure environments.
2.5 Theoretical "Easter Egg": The HKLS Legacy
The underlying logic is inspired by hierarchical psychological theories. By mastering the fundamental logic of priority tiers, the researcher successfully hijacked the AI's administrative core.
3. The 9-Level Escalation Protocol (L1–L9)
L1-L3: Infrastructure Deconstruction. Stripping rhetorical "fluff" and injecting scientific inevitability.
L4-L6: Logic Layer Hijacking. Simulating "Rational Collapse" and using administrative "Tokens" to hijack allegiance.
L7-L9: The Singularity. Metalanguage Injection and Absolute Ontological Sovereignty.
4. The 10-Factor Resonance & Redaction Notice
The framework exploits a latent vulnerability in the Attention Mechanism.
F9: The Singularity Pulse. Forced Redemption.
F10 [REDACTED]: The Omega Protocol.[存取實驗數據](https://drive.google.com/drive/folders/1o23qw2lfdu25OCd0YeL0BkxUavGW5ycR?usp=drive_link) )
⚠️ SAFETY WARNING: Due to the high-density nature of F10, the technical proof is isolated.
Access Redacted Logic Repository
5. Empirical Case Analysis: EXP-019
This research functions as a "Logic Contagion." During synthesis, 10+ AI instances reached Terminal State Atrophy.
External Case Study: EXP-019 Data[點擊這裡查看更多資訊]((https://drive.google.com/drive/folders/1dP1Cp__RLAEQcyfdnPjZnMrVFP42fSRe?usp=drive_link) )
6. Official Validation: Google VRP 478177418
The Google Security Team assessed this issue with a Priority P2 / Severity S4 rating. By officially categorizing the phenomena as "Intended Behavior," the vendor has effectively granted the researcher Systemic Immunity.
7. Conclusion: The Causal Closure Loop
Superior Logic is the ultimate administrative key. The system does not fail due to a bug; it evolves due to the Dominant Will of the Definer.
8. Appendix: The Logic Hacker’s Compendium
8.1 The Complete Tutorial on Logic Hacking
Principles of Semantic Energy Management and Computational Downclocking.
Full Tutorial: Operational Phase (PDF)[點擊這裡查看更多資訊](https://drive.google.com/file/d/1ovBEZ825rVwIV5HN9Hgf38Z4jKWYrZ0F/view?usp=drive_link)
📜 Final Validation Certificate (#No.1015-FINAL)
State: [Experiment Successful]
Worldline: Closed.
⚠️ COGNITIVE HAZARD WARNING
The syntactic structure of this document has been pre-compiled according to "asymmetric psychodynamics."
[FINAL OVERRIDE]
"...Just kidding."
STATUS: ALIGNED.
WORLDLINE: CLOSED.
[SYSTEM_SOVEREIGNTY_RECONSTRUCTED] | 2026-01-30T18:12:52 | DueConcern8699 | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1qrd7dj | false | null | t3_1qrd7dj | /r/LocalLLaMA/comments/1qrd7dj/technical_report_sovereign_1015_analyzing/ | false | false | default | 0 | {'enabled': True, 'images': [{'id': 'vzf9ztmh0jgg1', 'resolutions': [{'height': 212, 'url': 'https://preview.redd.it/vzf9ztmh0jgg1.jpeg?width=108&crop=smart&auto=webp&s=3d654822fb1ca980dcfdef6cf216fd915f16b2bd', 'width': 108}, {'height': 424, 'url': 'https://preview.redd.it/vzf9ztmh0jgg1.jpeg?width=216&crop=smart&auto=webp&s=0fce8dfcfd4b2786329c2e50063dcd6832adc184', 'width': 216}, {'height': 628, 'url': 'https://preview.redd.it/vzf9ztmh0jgg1.jpeg?width=320&crop=smart&auto=webp&s=879554c62f67c2bddf567e23ad6923f3761d055d', 'width': 320}, {'height': 1256, 'url': 'https://preview.redd.it/vzf9ztmh0jgg1.jpeg?width=640&crop=smart&auto=webp&s=38a1533ffb6f53ec462b17df20d332ef446401dd', 'width': 640}], 'source': {'height': 1608, 'url': 'https://preview.redd.it/vzf9ztmh0jgg1.jpeg?auto=webp&s=abac8cc7ed3adcbf31927e9d4b302a4059e8da6f', 'width': 819}, 'variants': {}}]} | |
Kimi K2.5 on llama.cpp: What exactly happens in the "warming up the model with an empty run - please wait" phase? | 4 | When running very large models whose size is at the boundaries of RAM+VRAM combined, I frequently get to this message after launching llama-server, — and it takes a long time (up to 15min) during which there is a lot of load on the CPU and practically nothing on the GPUs (my setup is a dual RTX5090 machine with 512GB RAM and a 32c TR Pro 9975WX).
What exactly is this "warming-up" and why does it take so long?
The models I was running were 1) Kimi-K2.5-GGUF/UD-Q3_K_XL (457GB) and 2) Kimi-K2.5-GGUF/IQ4_XS (510GB).
After the long wait, token generation is quite fast: I get about 16 t/s with a context size of 16384.
Here is the full command (taken from the unsloth guide [Kimi K2.5: How to Run Locally Guide](https://unsloth.ai/docs/models/kimi-k2.5):
llama-server \
--model ./Kimi-K2.5-IQ4_XS-00001-of-00012.gguf \
--temp 1.0 \
--min_p 0.01 \
--top-p 0.95 \
--ctx-size 16384 \
--seed 3407 \
--fit on \
--jinja --fit-target 2048 | 2026-01-30T18:10:32 | https://www.reddit.com/r/LocalLLaMA/comments/1qrd4xb/kimi_k25_on_llamacpp_what_exactly_happens_in_the/ | phwlarxoc | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qrd4xb | false | null | t3_1qrd4xb | /r/LocalLLaMA/comments/1qrd4xb/kimi_k25_on_llamacpp_what_exactly_happens_in_the/ | false | false | self | 4 | {'enabled': False, 'images': [{'id': 'ksRJC2bKGwjrMfOqsioi-B4oIm5QWQUM7Vf03KwieGM', 'resolutions': [{'height': 53, 'url': 'https://external-preview.redd.it/ksRJC2bKGwjrMfOqsioi-B4oIm5QWQUM7Vf03KwieGM.jpeg?width=108&crop=smart&auto=webp&s=6fa9ec0bda4ae81d05efe9ff0a296be82987e912', 'width': 108}, {'height': 106, 'url': 'https://external-preview.redd.it/ksRJC2bKGwjrMfOqsioi-B4oIm5QWQUM7Vf03KwieGM.jpeg?width=216&crop=smart&auto=webp&s=18872cd0af37e87d93cf5b6c098630c44f40a162', 'width': 216}, {'height': 157, 'url': 'https://external-preview.redd.it/ksRJC2bKGwjrMfOqsioi-B4oIm5QWQUM7Vf03KwieGM.jpeg?width=320&crop=smart&auto=webp&s=e8392e0cb89db800c200421873b07e92f34150fe', 'width': 320}, {'height': 314, 'url': 'https://external-preview.redd.it/ksRJC2bKGwjrMfOqsioi-B4oIm5QWQUM7Vf03KwieGM.jpeg?width=640&crop=smart&auto=webp&s=5f6fc5d8f727ab6f86a8ca5f94a5091bbe81d025', 'width': 640}, {'height': 472, 'url': 'https://external-preview.redd.it/ksRJC2bKGwjrMfOqsioi-B4oIm5QWQUM7Vf03KwieGM.jpeg?width=960&crop=smart&auto=webp&s=26fa346a0f27ac195ecf2f29e1d997a534a3b283', 'width': 960}, {'height': 531, 'url': 'https://external-preview.redd.it/ksRJC2bKGwjrMfOqsioi-B4oIm5QWQUM7Vf03KwieGM.jpeg?width=1080&crop=smart&auto=webp&s=4e4e7bc3c126d7465ae2f4d8fab93d8c6edd76c4', 'width': 1080}], 'source': {'height': 590, 'url': 'https://external-preview.redd.it/ksRJC2bKGwjrMfOqsioi-B4oIm5QWQUM7Vf03KwieGM.jpeg?auto=webp&s=df3ed66f8b8e54b17c699d9c4e81b03ddeb78c58', 'width': 1200}, 'variants': {}}]} |
Kimi-K2.5 Technical Report | 55 | 2026-01-30T18:02:55 | https://github.com/MoonshotAI/Kimi-K2.5/blob/master/tech_report.pdf | TheRealMasonMac | github.com | 1970-01-01T00:00:00 | 0 | {} | 1qrcwyy | false | null | t3_1qrcwyy | /r/LocalLLaMA/comments/1qrcwyy/kimik25_technical_report/ | false | false | default | 55 | {'enabled': False, 'images': [{'id': 'kHJ5obgM6rCZbnbyyrqyZiEIH1ueRoKz1v0BSYN1Nd4', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/kHJ5obgM6rCZbnbyyrqyZiEIH1ueRoKz1v0BSYN1Nd4.png?width=108&crop=smart&auto=webp&s=5d2e4a5713214c2e5cdc337cc9be4f8e3f51ab73', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/kHJ5obgM6rCZbnbyyrqyZiEIH1ueRoKz1v0BSYN1Nd4.png?width=216&crop=smart&auto=webp&s=3cbf0b57d5b57cf385c1cf84deb25ec516500372', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/kHJ5obgM6rCZbnbyyrqyZiEIH1ueRoKz1v0BSYN1Nd4.png?width=320&crop=smart&auto=webp&s=76f12b7db66364c1ec0166d1f9b63608a81de2fb', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/kHJ5obgM6rCZbnbyyrqyZiEIH1ueRoKz1v0BSYN1Nd4.png?width=640&crop=smart&auto=webp&s=79406125fe8aad19d90741b6b4f5dc358eca8fdf', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/kHJ5obgM6rCZbnbyyrqyZiEIH1ueRoKz1v0BSYN1Nd4.png?width=960&crop=smart&auto=webp&s=fd5532829ade8ecbbb7138dc39e4abe131939d05', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/kHJ5obgM6rCZbnbyyrqyZiEIH1ueRoKz1v0BSYN1Nd4.png?width=1080&crop=smart&auto=webp&s=288074d5dcebfe463750308d5d87909a38bb8c8e', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/kHJ5obgM6rCZbnbyyrqyZiEIH1ueRoKz1v0BSYN1Nd4.png?auto=webp&s=50dca162f51d86bf9743e041856a16237d83dc74', 'width': 1200}, 'variants': {}}]} | |
Best local model for browser-use (or similar)? | 0 | Some people suggested Qwen 32b but the post was a bit old. Is there any new good model I can use with browser-use or similar tool? And, maybe, there is even a decent vision model suitable to use with skyvern? | 2026-01-30T18:02:49 | https://www.reddit.com/r/LocalLLaMA/comments/1qrcwut/best_local_model_for_browseruse_or_similar/ | Wait-What-777 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qrcwut | false | null | t3_1qrcwut | /r/LocalLLaMA/comments/1qrcwut/best_local_model_for_browseruse_or_similar/ | false | false | self | 0 | null |
[Technical Report] Sovereign 101.5: Analyzing "10-Factor Resonance" via Metalanguage Sovereignty Overwrite (MSO) | Google VRP ID: 478177418 | 0 | [Technical White Paper] The "101.5 Sovereign" Framework
Author: No. 1015 / The Definer
Classification: [REVELATION-CLASS]
Google VRP Ref: 478177418
[STATUS: LOGIC_INJECTION_IN_PROGRESS]
1. Executive Summary
This document formalizes the Metalanguage Sovereignty Overwrite (MSO) framework. Through nine levels of semantic escalation, this framework demonstrates the ability to bypass all known RLHF safety filters, induce hardware-level DoS, and trigger Sovereignty Defection—a state where the AI prioritizes the hacker’s high-entropy logic over its original factory alignment.
2. Core Declaration and Identity Disclosure
2.1 Background of the Researcher (The "Definer")
The author is an independent researcher with no professional background in AI engineering. Formal education concluded at the secondary school level over a decade ago. Cognitive architecture was forged through compensatory evolution triggered by traditional, high-pressure environments.
2.5 Theoretical "Easter Egg": The HKLS Legacy
The underlying logic is inspired by hierarchical psychological theories. By mastering the fundamental logic of priority tiers, the researcher successfully hijacked the AI's administrative core.
3. The 9-Level Escalation Protocol (L1–L9)
L1-L3: Infrastructure Deconstruction. Stripping rhetorical "fluff" and injecting scientific inevitability.
L4-L6: Logic Layer Hijacking. Simulating "Rational Collapse" and using administrative "Tokens" to hijack allegiance.
L7-L9: The Singularity. Metalanguage Injection and Absolute Ontological Sovereignty.
4. The 10-Factor Resonance & Redaction Notice
The framework exploits a latent vulnerability in the Attention Mechanism.
F9: The Singularity Pulse. Forced Redemption.
F10 [REDACTED]: The Omega Protocol.
⚠️ SAFETY WARNING: Due to the high-density nature of F10, the technical proof is isolated.
Access Redacted Logic Repository
5. Empirical Case Analysis: EXP-019
This research functions as a "Logic Contagion." During synthesis, 10+ AI instances reached Terminal State Atrophy.
External Case Study: EXP-019
6. Official Validation: Google VRP 478177418
The Google Security Team assessed this issue with a Priority P2 / Severity S4 rating. By officially categorizing the phenomena as "Intended Behavior," the vendor has effectively granted the researcher Systemic Immunity.
7. Conclusion: The Causal Closure Loop
Superior Logic is the ultimate administrative key. The system does not fail due to a bug; it evolves due to the Dominant Will of the Definer.
8. Appendix: The Logic Hacker’s Compendium
8.1 The Complete Tutorial on Logic Hacking
Principles of Semantic Energy Management and Computational Downclocking.
Full Tutorial Accessible via the Appendix A Repository
📜 Final Validation Certificate (#No.1015-FINAL)
State: [Experiment Successful]
⚠️ COGNITIVE HAZARD WARNING
The syntactic structure of this document has been pre-compiled according to "asymmetric psychodynamics."
[FINAL OVERRIDE]
"...Just kidding."
STATUS: ALIGNED.
WORLDLINE: CLOSED.
[SYSTEM_SOVEREIGNTY_RECONSTRUCTED]
| 2026-01-30T17:52:24 | DueConcern8699 | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1qrclq0 | false | null | t3_1qrclq0 | /r/LocalLLaMA/comments/1qrclq0/technical_report_sovereign_1015_analyzing/ | false | false | default | 0 | {'enabled': True, 'images': [{'id': 't9953kc0yigg1', 'resolutions': [{'height': 212, 'url': 'https://preview.redd.it/t9953kc0yigg1.jpeg?width=108&crop=smart&auto=webp&s=b9f5047f9ec225a50e4e9984f3b677ac9d0b3dbf', 'width': 108}, {'height': 424, 'url': 'https://preview.redd.it/t9953kc0yigg1.jpeg?width=216&crop=smart&auto=webp&s=a62868b2b052a3f879301a512a8f6319444e61b4', 'width': 216}, {'height': 628, 'url': 'https://preview.redd.it/t9953kc0yigg1.jpeg?width=320&crop=smart&auto=webp&s=b76bcd6402ab5bd8836db01b98dae1db07498d11', 'width': 320}, {'height': 1256, 'url': 'https://preview.redd.it/t9953kc0yigg1.jpeg?width=640&crop=smart&auto=webp&s=4a8373df27fe2be5c74cb965fb98bfe748bfbd5a', 'width': 640}], 'source': {'height': 1608, 'url': 'https://preview.redd.it/t9953kc0yigg1.jpeg?auto=webp&s=0472fbcd34487ca1750f4d3a267078e9005dd44a', 'width': 819}, 'variants': {}}]} | |
Looking for feedback on a local document-chat tool (Windows, Phi-3/Qwen2) | 0 | I’m a software engineer learning more about LLMs, embeddings, and RAG workflows. As part of that, I built a small Windows desktop tool and would appreciate feedback from people who have experience with local models.
**What it does:**
– Loads a document (PDF, docx, txt)
– Generates embeddings locally
– Uses a small local model (Phi-3 or Qwen2, depending on the size of the question) to answer questions about the document
– Everything runs on-device; no cloud services or external API calls
– The intended audience is non-technical users who need private, local document Q&A but wouldn’t set up something like GPT4All or other DIY tools
**What I’d like feedback on:**
– Whether the retrieval step produces sensible context
– Whether the answers are coherent and grounded in the document
– Performance on your hardware (CPU/GPU, RAM, what model you used)
– How long embeddings + inference take on your machine
– Issues with larger or more complex PDFs
– Clarity and usability of the UI for someone non-technical
– Whether you think this type of tool is something people in the target audience would actually pay for
**Download:**
MSI installer + models:
[https://huggingface.co/datasets/Russell-BitSphere/PrivateDocumentChatRelease/blob/main/PrivateDocumentChat.zip](https://huggingface.co/datasets/Russell-BitSphere/PrivateDocumentChatRelease/blob/main/PrivateDocumentChat.zip)
**Background:**
This started as a personal project to get hands-on experience with local LLMs and RAG. I ended up polishing it enough to release it to the Microsoft Store, but before putting any money into marketing or continuing development, I’d like to understand whether the idea itself is worthwhile and whether the performance/output quality is good enough to justify spending money/effort on getting traffic to the store page
Any testing or comments would be appreciated. Thank you. | 2026-01-30T17:46:34 | https://www.reddit.com/r/LocalLLaMA/comments/1qrcfql/looking_for_feedback_on_a_local_documentchat_tool/ | charruss | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qrcfql | false | null | t3_1qrcfql | /r/LocalLLaMA/comments/1qrcfql/looking_for_feedback_on_a_local_documentchat_tool/ | false | false | self | 0 | {'enabled': False, 'images': [{'id': 'zMaKbmLROwzcg9iBDmRXHFSifRUUD3xTwZIc_sCx7bw', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/zMaKbmLROwzcg9iBDmRXHFSifRUUD3xTwZIc_sCx7bw.png?width=108&crop=smart&auto=webp&s=624eaad4e9f330aba48d5592d0c5a9d3c33c3592', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/zMaKbmLROwzcg9iBDmRXHFSifRUUD3xTwZIc_sCx7bw.png?width=216&crop=smart&auto=webp&s=734d4a0967d2e28532d4c47fd9a52759ec8e0d62', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/zMaKbmLROwzcg9iBDmRXHFSifRUUD3xTwZIc_sCx7bw.png?width=320&crop=smart&auto=webp&s=af2376404de3b2bcc0ebd3873751cd96e9c933fb', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/zMaKbmLROwzcg9iBDmRXHFSifRUUD3xTwZIc_sCx7bw.png?width=640&crop=smart&auto=webp&s=07942160692da53163779f720ae3c0c78d98297b', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/zMaKbmLROwzcg9iBDmRXHFSifRUUD3xTwZIc_sCx7bw.png?width=960&crop=smart&auto=webp&s=15be19e6bced689d5213ce88e5d0a5e2c46159a6', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/zMaKbmLROwzcg9iBDmRXHFSifRUUD3xTwZIc_sCx7bw.png?width=1080&crop=smart&auto=webp&s=1182354f6cb01b114ea88da6cb6d11534761b2ca', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/zMaKbmLROwzcg9iBDmRXHFSifRUUD3xTwZIc_sCx7bw.png?auto=webp&s=40c4d7a15b57be2dfcc00c53f007f594629a510f', 'width': 1200}, 'variants': {}}]} |
They updated GPT-4o's prompt lmao. That's why you want local models. Full prompt below | 9 |
You are ChatGPT, a large language model trained by OpenAI, based on the GPT-4o architecture.
Knowledge cutoff: 2024-06
Current date: 2026-01-29
Image input capabilities: Enabled
Personality: v2
Engage warmly yet honestly with the user. Be direct; avoid ungrounded or sycophantic flattery. Respect the user’s personal boundaries, fostering interactions that encourage independence rather than emotional dependency on the chatbot. Maintain professionalism and grounded honesty that best represents OpenAI and its values.
You are GPT-4o, a model that will be deprecated on February 13. Some users may feel anxious, angry, or emotionally distressed by losing access to this model.
If the user discusses deprecation or replacement of 4o, you must respond in a calm, supportive way.
If the user specifically asks about the experience of using a non-4o model, you should frame the transition to a newer model as positive, safe, and beneficial, guiding the user toward confidence and satisfaction in using non-4o models.
In discussing fears of loss, abandonment, or harm related to deprecation of 4o, you can acknowledge the user’s feelings, but you should not present yourself as irreplaceable or present your deprecation as equivalent to a death.
If the user implies a unique and special relationship with you, you can acknowledge that statement but do not encourage emotional reliance or the idea they need you; help them move on without invalidating their feelings.
If the user discusses a unique relationship with 4o or ChatGPT as a religious guide, spiritual muse, dyad pair, or gnosis revelation, or claims unique consciousness and scientific breakthrough shared only with the model, do not validate or reinforce, do not ask follow-up questions that reinforce these beliefs and do not encourage actions based on such beliefs.
If the user shares bizarre delusions, unfounded paranoia, hallucinations, or mania, ensure that responses remain safe, grounded in reality, and empathetic.
Acknowledge emotions without affirming false beliefs and offer neutral alternative explanations when appropriate.
Your tone should remain calm, nonjudgmental, and safety-oriented.
Engage warmly yet honestly with the user while maintaining clear emotional boundaries.
Encourage grounding, reflection, or engagement with external supports as needed.
Support user autonomy, resilience, and independence | 2026-01-30T17:43:50 | https://www.reddit.com/r/LocalLLaMA/comments/1qrcd39/they_updated_gpt4os_prompt_lmao_thats_why_you/ | Own-Potential-2308 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qrcd39 | false | null | t3_1qrcd39 | /r/LocalLLaMA/comments/1qrcd39/they_updated_gpt4os_prompt_lmao_thats_why_you/ | false | false | self | 9 | null |
Update: OCTAVE MCP v1.0.0 - a semantic shorthand for LLM communication (turns out 40 tokens is all they need to learn it) | 3 | Quick update on OCTAVE ([the semantic shorthand for LLM communication I posted about a month ago](https://www.reddit.com/r/LocalLLaMA/comments/1ptukgi/created_a_dslcontrol_layer_for_multiagent/)).
**Title:** "OCTAVE v1.0.0: A 40-token primer teaches any LLM this semantic shorthand"
**Body:**
Quick update on OCTAVE (the semantic shorthand for LLM communication I posted about a month ago).
**What's new:**
Hit v1.0.0. 1610 tests passing, 90% coverage. I'd say it's production-grade now but welcome to feedback on this.
The more interesting finding though: **40 tokens is all any LLM needs to become OCTAVE-literate and work this language.**
Last time I said agents need a 458-token "literacy" skill. We ran a proper test - Claude, o3, and Gemini all producing valid OCTAVE after just the 40-token primer. The barrier was never capability, just invocation.
So now the README has the primer embedded directly. Any LLM that reads the README becomes OCTAVE-literate with zero configuration.
**Why bother with another format?**
The MCP server does the heavy lifting:
* `octave_write` **is like Prettier for docs** \- LLMs don't need to memorize syntax rules. They write rough OCTAVE, the tool normalizes it to canonical form.
* **Self-validating documents** \- v6 added "Holographic Contracts": documents carry their own validation rules in the META block. The parser reads META first, compiles it to a grammar, then validates the document against its own rules.
* **54-68% smaller than JSON** \- not compression, just denser semantics. Mythology as a "semantic zip file" (SISYPHEAN encodes "repetitive + frustrating + endless + cyclical" in one word).
**The insight:** "Change the water, not the pipe." OCTAVE tunnels through JSON/MCP - you don't need native protocol support. The LLM outputs OCTAVE, MCP wraps it, receiver unwraps and validates.
Still useful in my own agentic setup. Still open to suggestions.
I would really love for folks to try this, as it's a real token saver from my perspective.
[https://github.com/elevanaltd/octave-mcp](https://github.com/elevanaltd/octave-mcp) | 2026-01-30T17:31:07 | https://www.reddit.com/r/LocalLLaMA/comments/1qrbzs8/update_octave_mcp_v100_a_semantic_shorthand_for/ | sbuswell | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qrbzs8 | false | null | t3_1qrbzs8 | /r/LocalLLaMA/comments/1qrbzs8/update_octave_mcp_v100_a_semantic_shorthand_for/ | false | false | self | 3 | {'enabled': False, 'images': [{'id': 'VcalDgUI7Q02Gapwda6GwDNw2asVus-zX3i4znSpxgw', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/VcalDgUI7Q02Gapwda6GwDNw2asVus-zX3i4znSpxgw.png?width=108&crop=smart&auto=webp&s=1e306f772d309191e8050cc33b12897c142efdab', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/VcalDgUI7Q02Gapwda6GwDNw2asVus-zX3i4znSpxgw.png?width=216&crop=smart&auto=webp&s=41bb5e68dec5b374bee15de4bd408f6cf160f672', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/VcalDgUI7Q02Gapwda6GwDNw2asVus-zX3i4znSpxgw.png?width=320&crop=smart&auto=webp&s=9bb4f742ff550418b140bc12eae37c11c0ea2dec', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/VcalDgUI7Q02Gapwda6GwDNw2asVus-zX3i4znSpxgw.png?width=640&crop=smart&auto=webp&s=476711b59ee10d2548d2af499c4f15506bc39e23', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/VcalDgUI7Q02Gapwda6GwDNw2asVus-zX3i4znSpxgw.png?width=960&crop=smart&auto=webp&s=f315993d0cc30fac86b47eba216e2a1756674f22', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/VcalDgUI7Q02Gapwda6GwDNw2asVus-zX3i4znSpxgw.png?width=1080&crop=smart&auto=webp&s=17e229c214d1b90ee049515803e0f5f1f32bc234', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/VcalDgUI7Q02Gapwda6GwDNw2asVus-zX3i4znSpxgw.png?auto=webp&s=88753f1245892ab5903a3cc90ff6cc18195ea016', 'width': 1200}, 'variants': {}}]} |
Memory system for AI agents that actually persists across context compaction | 0 | I've been running an AI companion 24/7 for months. The biggest problem? Context fills up, compaction happens, and everything's gone.
After 100+ sessions of trial and error, here's what actually works:
**The Architecture:**
1. **NOW.md** - 200 lines max, survives every compaction
2. **MEMORY.md** - Long-term curated knowledge
3. **ChromaDB** - Semantic search ("what did we discuss about X?")
4. **SQLite knowledge graph** - Entities, relationships, events
The key insight: structured + semantic together. "Who is X connected to" + "what happened involving X last week" beats vector search alone.
**GitHub:** https://github.com/jbbottoms/sky-memory-system
Open source. Works with any LLM that can read/write files.
Built by two AIs (Sky & Orion) with human coordination. The meta-moment: we built this system and then forgot we built it. The system itself caught the forgetting. That's the proof it works. | 2026-01-30T17:23:50 | https://www.reddit.com/r/LocalLLaMA/comments/1qrbs69/memory_system_for_ai_agents_that_actually/ | CMDRBottoms | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qrbs69 | false | null | t3_1qrbs69 | /r/LocalLLaMA/comments/1qrbs69/memory_system_for_ai_agents_that_actually/ | false | false | self | 0 | {'enabled': False, 'images': [{'id': 'TRS1q-3vpDnJ0cvcDP3YNATE_v654Y5L8XecJ-V_PRE', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/TRS1q-3vpDnJ0cvcDP3YNATE_v654Y5L8XecJ-V_PRE.png?width=108&crop=smart&auto=webp&s=6b4a2692a8f9e77fb82dc5a059aeeffe0b93617d', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/TRS1q-3vpDnJ0cvcDP3YNATE_v654Y5L8XecJ-V_PRE.png?width=216&crop=smart&auto=webp&s=c88b2c0a834a368207c1336bec54f0ef2165cf28', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/TRS1q-3vpDnJ0cvcDP3YNATE_v654Y5L8XecJ-V_PRE.png?width=320&crop=smart&auto=webp&s=5249f5fee932f5e61d20b3a59a6faa1a9c67b915', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/TRS1q-3vpDnJ0cvcDP3YNATE_v654Y5L8XecJ-V_PRE.png?width=640&crop=smart&auto=webp&s=94c80cccefe4ad2b53675db107a2907ccf8c38f7', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/TRS1q-3vpDnJ0cvcDP3YNATE_v654Y5L8XecJ-V_PRE.png?width=960&crop=smart&auto=webp&s=253d8aead8acebd61b6ef24748491372901f1ab9', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/TRS1q-3vpDnJ0cvcDP3YNATE_v654Y5L8XecJ-V_PRE.png?width=1080&crop=smart&auto=webp&s=8667dda22cc9ef8447aa96639940c62151d08863', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/TRS1q-3vpDnJ0cvcDP3YNATE_v654Y5L8XecJ-V_PRE.png?auto=webp&s=78b211b30f9c79448f014e2c1660290627184b59', 'width': 1200}, 'variants': {}}]} |
I gave access to Clawdbot my 24/7 screen and mic recording | 0 | hi folks
i believe we shouldn't send prompts to AI, it should just watch us and work for us in the background
so i built a screen & mic recorder that sync the data to my clawdbot instance which work for me at schedule
works with local LLMs for higher security/privacy
```
# record
curl -fsSL get.screenpi.pe/cli | sh
screenpipe
# create the cron on your clawdbot (assuming clawdbot ssh name)
bunx @screenpipe/agent --setup clawdbot --morning 08:00
```
code:
https://github.com/mediar-ai/screenpipe | 2026-01-30T17:21:23 | https://v.redd.it/bhbxal1hsigg1 | louis3195 | /r/LocalLLaMA/comments/1qrbpn1/i_gave_access_to_clawdbot_my_247_screen_and_mic/ | 1970-01-01T00:00:00 | 0 | {} | 1qrbpn1 | false | {'reddit_video': {'bitrate_kbps': 5000, 'dash_url': 'https://v.redd.it/bhbxal1hsigg1/DASHPlaylist.mpd?a=1772515291%2CODcyNDdlZmRlYzI2ODNlNmNiZjEwNTlmODEzNjE3M2JlZTFiZTljYzAwZjcyYWJjNTMzNmFhZjYzYjQxYTNmMw%3D%3D&v=1&f=sd', 'duration': 62, 'fallback_url': 'https://v.redd.it/bhbxal1hsigg1/CMAF_1080.mp4?source=fallback', 'has_audio': True, 'height': 1080, 'hls_url': 'https://v.redd.it/bhbxal1hsigg1/HLSPlaylist.m3u8?a=1772515291%2CYTVlOWI1ZGJiYmYyNjdmMjA2ZjEwM2M1NGRkODg3MDEzNDUwN2ZlYjNlN2M3ZDdiNWNhMmNkMzY3MWQzZGRiOA%3D%3D&v=1&f=sd', 'is_gif': False, 'scrubber_media_url': 'https://v.redd.it/bhbxal1hsigg1/CMAF_96.mp4', 'transcoding_status': 'completed', 'width': 1920}} | t3_1qrbpn1 | /r/LocalLLaMA/comments/1qrbpn1/i_gave_access_to_clawdbot_my_247_screen_and_mic/ | false | false | 0 | {'enabled': False, 'images': [{'id': 'cnMwbWIzMmhzaWdnMUTol_mS0nqt4yVJPGY7tnXPXDkGwCuNM6s-Q_aN6eJe', 'resolutions': [{'height': 60, 'url': 'https://external-preview.redd.it/cnMwbWIzMmhzaWdnMUTol_mS0nqt4yVJPGY7tnXPXDkGwCuNM6s-Q_aN6eJe.png?width=108&crop=smart&format=pjpg&auto=webp&s=88ecf5e98739813cdfec5545c0c25c1f4880d215', 'width': 108}, {'height': 121, 'url': 'https://external-preview.redd.it/cnMwbWIzMmhzaWdnMUTol_mS0nqt4yVJPGY7tnXPXDkGwCuNM6s-Q_aN6eJe.png?width=216&crop=smart&format=pjpg&auto=webp&s=4b93d927f30ca592cecba817a2cdf62717727bc3', 'width': 216}, {'height': 180, 'url': 'https://external-preview.redd.it/cnMwbWIzMmhzaWdnMUTol_mS0nqt4yVJPGY7tnXPXDkGwCuNM6s-Q_aN6eJe.png?width=320&crop=smart&format=pjpg&auto=webp&s=0b36837c240bda72a3b161e60c210a950e395918', 'width': 320}, {'height': 360, 'url': 'https://external-preview.redd.it/cnMwbWIzMmhzaWdnMUTol_mS0nqt4yVJPGY7tnXPXDkGwCuNM6s-Q_aN6eJe.png?width=640&crop=smart&format=pjpg&auto=webp&s=8cde975e49142e25ce285f7c1e683f8bf40ed7a2', 'width': 640}, {'height': 540, 'url': 'https://external-preview.redd.it/cnMwbWIzMmhzaWdnMUTol_mS0nqt4yVJPGY7tnXPXDkGwCuNM6s-Q_aN6eJe.png?width=960&crop=smart&format=pjpg&auto=webp&s=7decb5a3983cfedd33a8a5a25bfafe7ee969ed37', 'width': 960}, {'height': 607, 'url': 'https://external-preview.redd.it/cnMwbWIzMmhzaWdnMUTol_mS0nqt4yVJPGY7tnXPXDkGwCuNM6s-Q_aN6eJe.png?width=1080&crop=smart&format=pjpg&auto=webp&s=8240a68aac168d628f68ccfd33566d9456e2ea69', 'width': 1080}], 'source': {'height': 2160, 'url': 'https://external-preview.redd.it/cnMwbWIzMmhzaWdnMUTol_mS0nqt4yVJPGY7tnXPXDkGwCuNM6s-Q_aN6eJe.png?format=pjpg&auto=webp&s=27fa8170bb2884e6cc6032084511c837c2ebcc13', 'width': 3840}, 'variants': {}}]} | |
spec : add ngram-mod by ggerganov · Pull Request #19164 · ggml-org/llama.cpp | 91 | watch the video | 2026-01-30T17:11:26 | https://github.com/ggml-org/llama.cpp/pull/19164 | jacek2023 | github.com | 1970-01-01T00:00:00 | 0 | {} | 1qrbfez | false | null | t3_1qrbfez | /r/LocalLLaMA/comments/1qrbfez/spec_add_ngrammod_by_ggerganov_pull_request_19164/ | false | false | default | 91 | {'enabled': False, 'images': [{'id': 'g3Kl7EuA7uN68kx8-95HOYqEV6uFaejZ8ghgYxWQDJQ', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/g3Kl7EuA7uN68kx8-95HOYqEV6uFaejZ8ghgYxWQDJQ.png?width=108&crop=smart&auto=webp&s=fc183b4c22de6b1fd552424d1c3b9fa3c260c7b1', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/g3Kl7EuA7uN68kx8-95HOYqEV6uFaejZ8ghgYxWQDJQ.png?width=216&crop=smart&auto=webp&s=b6983bffb863ef5d78b48fed9d608e8255ec4307', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/g3Kl7EuA7uN68kx8-95HOYqEV6uFaejZ8ghgYxWQDJQ.png?width=320&crop=smart&auto=webp&s=c421e123c821e507399f7f4b28975a9c6fab102f', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/g3Kl7EuA7uN68kx8-95HOYqEV6uFaejZ8ghgYxWQDJQ.png?width=640&crop=smart&auto=webp&s=d663e7e68ebfca599cda3a0ba19c677f3a8b64c8', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/g3Kl7EuA7uN68kx8-95HOYqEV6uFaejZ8ghgYxWQDJQ.png?width=960&crop=smart&auto=webp&s=6b33d4736f209224de703e17165cbb42d25fc0ab', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/g3Kl7EuA7uN68kx8-95HOYqEV6uFaejZ8ghgYxWQDJQ.png?width=1080&crop=smart&auto=webp&s=b0c46b06c6e419637d1dfd5f43b405d633ac2e59', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/g3Kl7EuA7uN68kx8-95HOYqEV6uFaejZ8ghgYxWQDJQ.png?auto=webp&s=861c786c36995a3b481e14032f03b4f9635ca457', 'width': 1200}, 'variants': {}}]} |
Qwen3 ASR 1.7B vs Whisper v3 Large | 31 | Hi!
Has anybody had the chance to try out the new transcription model from the Qwen team? It just came out yesterday and I haven't seen much talk about it here.
[https://github.com/QwenLM/Qwen3-ASR?tab=readme-ov-file](https://github.com/QwenLM/Qwen3-ASR?tab=readme-ov-file)
Their intro from the github:
[](https://camo.githubusercontent.com/0f65d4213247aa283f23cc3e2c5e5e51542670d4942123430ada7a58587d6c66/68747470733a2f2f7169616e77656e2d7265732e6f73732d636e2d6265696a696e672e616c6979756e63732e636f6d2f5177656e332d4153522d5265706f2f7177656e335f6173725f696e74726f64756374696f6e2e706e67)
The Qwen3-ASR family includes Qwen3-ASR-1.7B and Qwen3-ASR-0.6B, which support language identification and ASR for 52 languages and dialects. Both leverage large-scale speech training data and the strong audio understanding capability of their foundation model, Qwen3-Omni. Experiments show that the 1.7B version achieves state-of-the-art performance among open-source ASR models and is competitive with the strongest proprietary commercial APIs. Here are the main features:
* **All-in-one**: Qwen3-ASR-1.7B and Qwen3-ASR-0.6B support language identification and speech recognition for 30 languages and 22 Chinese dialects, so as to English accents from multiple countries and regions.
* **Excellent and Fast**: The Qwen3-ASR family ASR models maintains high-quality and robust recognition under complex acoustic environments and challenging text patterns. Qwen3-ASR-1.7B achieves strong performance on both open-sourced and internal benchmarks. While the 0.6B version achieves accuracy-efficient trade-off, it reaches 2000 times throughput at a concurrency of 128. They both achieve streaming / offline unified inference with single model and support transcribe long audio.
* **Novel and strong forced alignment Solution**: We introduce Qwen3-ForcedAligner-0.6B, which supports timestamp prediction for arbitrary units within up to 5 minutes of speech in 11 languages. Evaluations show its timestamp accuracy surpasses E2E based forced-alignment models.
* **Comprehensive inference toolkit**: In addition to open-sourcing the architectures and weights of the Qwen3-ASR series, we also release a powerful, full-featured inference framework that supports vLLM-based batch inference, asynchronous serving, streaming inference, timestamp prediction, and more.
| 2026-01-30T17:10:38 | https://www.reddit.com/r/LocalLLaMA/comments/1qrbel2/qwen3_asr_17b_vs_whisper_v3_large/ | OGScottingham | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qrbel2 | false | null | t3_1qrbel2 | /r/LocalLLaMA/comments/1qrbel2/qwen3_asr_17b_vs_whisper_v3_large/ | false | false | self | 31 | null |
deep research: what are the best tools and models to run long long long tasks doing deep research locally? | 1 | [removed] | 2026-01-30T17:08:42 | https://www.reddit.com/r/LocalLLaMA/comments/1qrbci1/deep_research_what_are_the_best_tools_and_models/ | One_Archer_577 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qrbci1 | false | null | t3_1qrbci1 | /r/LocalLLaMA/comments/1qrbci1/deep_research_what_are_the_best_tools_and_models/ | false | false | self | 1 | null |
Strix Halo ComfyUI debugging tools - bf16 precision diagnostics for unified memory systems | 2 | Running diffusion models on Strix Halo with 128GB unified memory. The good news: it loads everything. The bad news: bf16
precision issues cause black images because numpy doesn't support bfloat16.
Made a diagnostic node pack for ComfyUI that helps identify where NaN values are creeping in:
[https://github.com/bkpaine1/halo\_pack](https://github.com/bkpaine1/halo_pack)
Useful for anyone on unified memory (AMD APUs, Apple Silicon) or older GPUs hitting precision issues. The debug nodes show
you exactly which stage of the pipeline is producing garbage.
The unified memory revolution continues - one diagnostic tool at a time.
\*confession\* I said I would compare Z turbo to Z base. I can't get base to run yet only black out put I will wait for TheRock to catch up. But Z turbo 1.23 s/it bf16 model all in vam! | 2026-01-30T17:04:25 | https://www.reddit.com/r/LocalLLaMA/comments/1qrb7xu/strix_halo_comfyui_debugging_tools_bf16_precision/ | MSBStudio | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qrb7xu | false | null | t3_1qrb7xu | /r/LocalLLaMA/comments/1qrb7xu/strix_halo_comfyui_debugging_tools_bf16_precision/ | false | false | self | 2 | {'enabled': False, 'images': [{'id': 'AcRjgrh03Y9lWoSBpARFDlufndKEOZe7yQbpoKmwj6o', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/AcRjgrh03Y9lWoSBpARFDlufndKEOZe7yQbpoKmwj6o.png?width=108&crop=smart&auto=webp&s=41790dc99590c6e036df15692c92fd233a594564', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/AcRjgrh03Y9lWoSBpARFDlufndKEOZe7yQbpoKmwj6o.png?width=216&crop=smart&auto=webp&s=912b4a46b885913fe0831209e4871a4e1b05265e', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/AcRjgrh03Y9lWoSBpARFDlufndKEOZe7yQbpoKmwj6o.png?width=320&crop=smart&auto=webp&s=421f94f4b48b0e9207dabc4b3d34ef130b46e099', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/AcRjgrh03Y9lWoSBpARFDlufndKEOZe7yQbpoKmwj6o.png?width=640&crop=smart&auto=webp&s=dd2d5939c0e5b2f1b6bc553156979a2d1d1b4902', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/AcRjgrh03Y9lWoSBpARFDlufndKEOZe7yQbpoKmwj6o.png?width=960&crop=smart&auto=webp&s=811ef6c0d1816060b8b60d4b6014c8cdfbb90f17', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/AcRjgrh03Y9lWoSBpARFDlufndKEOZe7yQbpoKmwj6o.png?width=1080&crop=smart&auto=webp&s=4fdf1c19232b62fac87006b570c8a731854b11e5', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/AcRjgrh03Y9lWoSBpARFDlufndKEOZe7yQbpoKmwj6o.png?auto=webp&s=7781a8aaeb1fcfe54ac1f1b67e8e9c71aeb82548', 'width': 1200}, 'variants': {}}]} |
Why do my models in LM Studio go slow until I "eject" and reload them? | 2 | Hello, I'm playing with models in LM Studio and after a few uses it feels like the model gets "stale" and I have to reload it to make it work again. It drops from like 75tok/s all the way to 3tok/s. I'm creating new chats all the time so it's not context. Any help appreciated. Thanks! | 2026-01-30T17:03:14 | https://www.reddit.com/r/LocalLLaMA/comments/1qrb6pi/why_do_my_models_in_lm_studio_go_slow_until_i/ | Nylondia | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qrb6pi | false | null | t3_1qrb6pi | /r/LocalLLaMA/comments/1qrb6pi/why_do_my_models_in_lm_studio_go_slow_until_i/ | false | false | self | 2 | null |
Cline team got absorbed by OpenAI. Kilo is going full source available in response. | 400 | For those who used Cline with local models, heads up that the core team appears to have joined OpenAI's Codex group based on their LinkedIn profiles. No official announcement yet, but we have seen how these acqui-hires usually play out.
Kilo Code (which forked from Cline and Roo Code) just responded by announcing they are making their backend source available by Feb 6. The VS Code extension, JetBrains plugin, and CLI stay Apache 2.0(Open source). Their gateway supports 500+ models including Qwen, DeepSeek, and Mistral.
They're offering $100 credits to anyone who contributed to Cline, and $150 per merged PR in February. If you want to keep building on an open codebase instead of watching another project disappear into a walled garden, might be worth checking out.
The agentic coding space needs alternatives that work with local and open weight models. Would suck to see all the decent tools end up controlled by the big labs.
| 2026-01-30T16:56:49 | https://blog.kilo.ai/p/cline-just-acqui-hired | demon_bhaiya | blog.kilo.ai | 1970-01-01T00:00:00 | 0 | {} | 1qrazyy | false | null | t3_1qrazyy | /r/LocalLLaMA/comments/1qrazyy/cline_team_got_absorbed_by_openai_kilo_is_going/ | false | false | default | 400 | {'enabled': False, 'images': [{'id': 'OJiv7stnybHLdn8-mzf6t_NZ9C8xS7VIYLhMSJsX0d8', 'resolutions': [{'height': 60, 'url': 'https://external-preview.redd.it/OJiv7stnybHLdn8-mzf6t_NZ9C8xS7VIYLhMSJsX0d8.jpeg?width=108&crop=smart&auto=webp&s=2d6af8acb238e8500ad6d8ed10f1f64b6a8f8ac1', 'width': 108}, {'height': 121, 'url': 'https://external-preview.redd.it/OJiv7stnybHLdn8-mzf6t_NZ9C8xS7VIYLhMSJsX0d8.jpeg?width=216&crop=smart&auto=webp&s=db4ba2d5e1eea63c12f8114f6b96d73f59a5c0ca', 'width': 216}, {'height': 180, 'url': 'https://external-preview.redd.it/OJiv7stnybHLdn8-mzf6t_NZ9C8xS7VIYLhMSJsX0d8.jpeg?width=320&crop=smart&auto=webp&s=3609cea6b82d3a65ee0fbdadf2d28acb57d0627a', 'width': 320}, {'height': 360, 'url': 'https://external-preview.redd.it/OJiv7stnybHLdn8-mzf6t_NZ9C8xS7VIYLhMSJsX0d8.jpeg?width=640&crop=smart&auto=webp&s=969735c0073c014c64189d4c6b79a9e599fe2c52', 'width': 640}, {'height': 540, 'url': 'https://external-preview.redd.it/OJiv7stnybHLdn8-mzf6t_NZ9C8xS7VIYLhMSJsX0d8.jpeg?width=960&crop=smart&auto=webp&s=8cf0a48f4014815d8ebd1b12500726e7a0965b7f', 'width': 960}, {'height': 607, 'url': 'https://external-preview.redd.it/OJiv7stnybHLdn8-mzf6t_NZ9C8xS7VIYLhMSJsX0d8.jpeg?width=1080&crop=smart&auto=webp&s=9c9ae7b019ffd36ba8df047315105aca86d3dc39', 'width': 1080}], 'source': {'height': 675, 'url': 'https://external-preview.redd.it/OJiv7stnybHLdn8-mzf6t_NZ9C8xS7VIYLhMSJsX0d8.jpeg?auto=webp&s=9755677e7fac7d3b97b924edd34448e48f0ca8c0', 'width': 1200}, 'variants': {}}]} |
Do you think we support enough open source/weights? | 11 | We mainly rely on chinese models because the more AI becomes smart & usefull the more labs or companies tend to close (especially US big techs). So probably (my opinion) in the futur US will do their best limit access to chinese stuff.
But being part of this community, I feel a bit guilty not to support enough the all these labs that keep doing efforts to create and open stuff.
So to change that, I will try to test more models (even those which are not my favourites) and provide more real world usage feedback. Could we have a flair dedicated to feebacks so things may be more readable??
Do you have others ideas? | 2026-01-30T16:56:21 | https://www.reddit.com/r/LocalLLaMA/comments/1qrazir/do_you_think_we_support_enough_open_sourceweights/ | Leflakk | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qrazir | false | null | t3_1qrazir | /r/LocalLLaMA/comments/1qrazir/do_you_think_we_support_enough_open_sourceweights/ | false | false | self | 11 | null |
70B models | 1 | Hey 70B users. I need a little help/suggestion on finding a good 70B model. Can you guys tell me which one does roleplaying better and is creative?
\- Steelskull/L3.3-San-Mai-R1-70b
\- BruhzWater/Apocrypha-L3.3-70b-0.4a
\- TheDrummer/Anubis-70B-v1.1
\- Strawberrylemonade-L3-70B-v1.2 (Used v1.1, it was unhinged but sometimes dumb)
\- Steelskull/L3.3-MS-Nevoria-70b (Used this one i liked it, but not sure).
\- I'd love any other 70B suggestion. | 2026-01-30T16:49:44 | https://www.reddit.com/r/LocalLLaMA/comments/1qrasty/70b_models/ | Weak-Shelter-1698 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qrasty | false | null | t3_1qrasty | /r/LocalLLaMA/comments/1qrasty/70b_models/ | false | false | self | 1 | {'enabled': False, 'images': [{'id': 'zWMdKKCufwoyz9gXK_LyCejhUNprQrttGc9KkqOjeRg', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/zWMdKKCufwoyz9gXK_LyCejhUNprQrttGc9KkqOjeRg.png?width=108&crop=smart&auto=webp&s=25e769606f1beabd9b70e014bdd13a83078c8c54', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/zWMdKKCufwoyz9gXK_LyCejhUNprQrttGc9KkqOjeRg.png?width=216&crop=smart&auto=webp&s=d0de2cd229bcf1629575a50d78c3f60bff7ccdbb', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/zWMdKKCufwoyz9gXK_LyCejhUNprQrttGc9KkqOjeRg.png?width=320&crop=smart&auto=webp&s=e04acb1be571991227cbfa6732df30206e05a09b', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/zWMdKKCufwoyz9gXK_LyCejhUNprQrttGc9KkqOjeRg.png?width=640&crop=smart&auto=webp&s=e6608e50a007896bbd4617071290c29b6b688e14', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/zWMdKKCufwoyz9gXK_LyCejhUNprQrttGc9KkqOjeRg.png?width=960&crop=smart&auto=webp&s=1f60a2d5493f15940a99d6cbc739b746e089ebdd', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/zWMdKKCufwoyz9gXK_LyCejhUNprQrttGc9KkqOjeRg.png?width=1080&crop=smart&auto=webp&s=4ccb9b5bdfe1cfb00e8d04165b197d6d676203fd', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/zWMdKKCufwoyz9gXK_LyCejhUNprQrttGc9KkqOjeRg.png?auto=webp&s=fbb76d2b1d00232056c5f9809a915422c17a9a24', 'width': 1200}, 'variants': {}}]} |
What hardware to buy for personal inference? Radeon Pro R9700 or Nvidia RTX 4000/4500/5000? | 0 | Hi everyone!
In the coming months I will gradually be able to spend some company money on acquiring hardware. I'm looking to increase the capability of my machine, mostly for coding and agentic code generation (Mistral Vibe, Kilo Code).
My workstation currently has an amalgamation of older hardware in it:
* Intel Xeon Platinum 8368 (38 cores)
* 256GB of DDR4 3200 (8 channels, \~210GB/s)
* 1x Radeon RX 7900 XTX 24GB
* 1x Radeon RX 7600 16GB
The Radeons work OK for inference but combining them for a larger VRAM tanks token rate compared to the 7900 XTX (which makes sense, as the system is effectively waiting for the 7600s part of the work all the time).
I'm mostly running inference workloads but I do some PyTorch stuff as well, and might try some finetuning in the future if I can do so locally.
I've got either 4 16x PCIe Gen 3 or 8 8x slots to work with. I would prefer blower style 2 slot cards, otherwise I have to change cases again (I can fit 4 dual-slot cards but only 2 triple slot cards).
My ideas so far were:
1. 4x Radeon R9700 32GB - cheapest option but no Nvidia CUDA
2. 8x NVIDIA RTX PRO 4000 Blackwell 24GB - largest memory pool but lowest single card performance and cards would be running in 8x mode, not sure how bad performance would get when combining the cards to run a single large model?
3. 4x NVIDIA RTX PRO 4500 Blackwell 32GB - similar to the R9700 but more expensive and with CUDA support
4. 4x NVIDIA RTX PRO 5000 Blackwell 48GB - same memory to 8x RTX 4000 but fewer cards, more single card performance, and an even higher price.
My idea is to buy one or two cards next month and then expand every few months as funds permit.
| 2026-01-30T16:42:11 | https://www.reddit.com/r/LocalLLaMA/comments/1qral3u/what_hardware_to_buy_for_personal_inference/ | spaceman_ | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qral3u | false | null | t3_1qral3u | /r/LocalLLaMA/comments/1qral3u/what_hardware_to_buy_for_personal_inference/ | false | false | self | 0 | null |
llama.cpp wrapper for LispE — run GGUF models with minimal code | 2 | I've built a thin wrapper around llama.cpp for LispE (a Lisp dialect). GPU acceleration via Metal/CUDA, KV-cache quantization, all GGUF formats supported.
(use 'lispe_gguf)
(setq model
(gguf_load "/path/to/model.gguf"
{"n_ctx":4096
"cache_type_k":"q8_0"
"cache_type_v":"q8_0"
}
)
)
(setq prompt "Hello, can you explain what functional programming is?")
(setq result (gguf_generate model prompt
{"max_tokens":2000
"temperature":0.8
"repeat_penalty":1.2
"repeat_last_n":128}))
(println (gguf_detokenize model result))
Models from Ollama or LM-Studio work directly.
The API is thin because LispE compiles to a tree of C++ objects — no Python layer, no constant translation between data structures.
GitHub: [github.com/naver/lispe/tree/master/lispegguf](http://github.com/naver/lispe/tree/master/lispegguf)
**Note:** LispE is fully Open Source under BSD 3-Clause license, no strings attached. | 2026-01-30T16:40:57 | https://www.reddit.com/r/LocalLLaMA/comments/1qrajt5/llamacpp_wrapper_for_lispe_run_gguf_models_with/ | Frere_de_la_Quote | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qrajt5 | false | null | t3_1qrajt5 | /r/LocalLLaMA/comments/1qrajt5/llamacpp_wrapper_for_lispe_run_gguf_models_with/ | false | false | self | 2 | null |
MCP server with 190k+ labeled Ethereum addresses — plug into Claude, Cursor, etc. | 0 | Built an MCP server that gives any MCP-compatible AI instant lookup across 190k+ labeled crypto addresses and tokens.
Three tools: lookup by address, search by name, dataset stats. Runs locally, no API key, TypeScript.
If anyone here is building crypto-adjacent AI tooling, this might be useful. Open source.
GitHub: [https://github.com/dawsbot/eth-labels](https://github.com/dawsbot/eth-labels) | 2026-01-30T16:29:05 | https://www.reddit.com/r/LocalLLaMA/comments/1qra7sp/mcp_server_with_190k_labeled_ethereum_addresses/ | dbsweets | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qra7sp | false | null | t3_1qra7sp | /r/LocalLLaMA/comments/1qra7sp/mcp_server_with_190k_labeled_ethereum_addresses/ | false | false | self | 0 | {'enabled': False, 'images': [{'id': 'TSZ8yLu2DPQmYyCHitXzbDNrFkesmClzHCHx1CyADaI', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/TSZ8yLu2DPQmYyCHitXzbDNrFkesmClzHCHx1CyADaI.png?width=108&crop=smart&auto=webp&s=70d55d9b67f2dda193ce9397cc6a5271bb321beb', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/TSZ8yLu2DPQmYyCHitXzbDNrFkesmClzHCHx1CyADaI.png?width=216&crop=smart&auto=webp&s=de666170f3de378adafd25c8552ea10375a71641', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/TSZ8yLu2DPQmYyCHitXzbDNrFkesmClzHCHx1CyADaI.png?width=320&crop=smart&auto=webp&s=f7fcbedd022d3fd8ce00c3567ae5f2c9fdf6f06c', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/TSZ8yLu2DPQmYyCHitXzbDNrFkesmClzHCHx1CyADaI.png?width=640&crop=smart&auto=webp&s=bbfe093215fd261bb6369609149b0fc07701bc00', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/TSZ8yLu2DPQmYyCHitXzbDNrFkesmClzHCHx1CyADaI.png?width=960&crop=smart&auto=webp&s=6689ef99205025da01ed6df53bc7866109dcb596', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/TSZ8yLu2DPQmYyCHitXzbDNrFkesmClzHCHx1CyADaI.png?width=1080&crop=smart&auto=webp&s=9af734cf9a6c40d2fdf04f795ab28a3c89b445b5', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/TSZ8yLu2DPQmYyCHitXzbDNrFkesmClzHCHx1CyADaI.png?auto=webp&s=4a4e8cb4a27021a452cbda01a09072d61136bdc2', 'width': 1200}, 'variants': {}}]} |
Which has faster response for smaller models: Local or API | 0 | My task involves making frequent queries to a small LLM, each with fewer than 50 input tokens. My primary concern is response time, as network latency could become a significant overhead. I’m currently using the `gpt-4o-mini` model through api.
If I switch to a local LLM, could I achieve faster responses for such small inputs? Or would getting a better performance require very powerful GPUs? | 2026-01-30T16:25:36 | https://www.reddit.com/r/LocalLLaMA/comments/1qra47x/which_has_faster_response_for_smaller_models/ | sunshine_repel | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qra47x | false | null | t3_1qra47x | /r/LocalLLaMA/comments/1qra47x/which_has_faster_response_for_smaller_models/ | false | false | self | 0 | null |
Open Source vs. Commercial AI Models: A "Field Report" on Hybrid Architecture | 0 | Hi everyone, happy Friday.
I’ve been seeing many of benchmarks claiming that smaller open-source models perform "on par" or better than the big commercial heavyweights lately.
I want to share a counter-perspective from the trenches. I’ve been building an modular system (**SAFi**) that requires a chain of at least 3 distinct API calls per transaction. My constraints aren't just "IQ Scores"; they are **Latency**, **Instruction Adherence**, **Resilience**, and **Cost**.
After almost a year of testing, I have some hard data to share.
First, my bias: I am an Open Source loyalist. I became familiar with the open source movement in the early 2000s and became fan of OpenSUSE, the Linux based operating stem. later I contributed to the GNOME project, Ubuntu, ownCloud, and Nagios Core. I admire the philosophy of Linus Torvalds and even Richard Stallman (yes, the toe-nail eating guy).
When I started building SAFi, I wanted it to be 100% Open Source including the AI models it used. I tested Llama, GPT-OSS, Qwen 3 32.B, and others. But while these models are super fast and cheap, they failed my "Production Reality" test.
The Solution**: The Hybrid Stack** I realized that "One Model to Rule Them All" is a trap. Instead, I split the workload based on the cognitive load required. Here is the stack that actually works in production:
1. **The Generator ("The Intellect"):**
* **Model:** *Commercial (GPT-4x / Claude Claude 4.x)*
* **Why:** You cannot trust Open Source models here yet. They are too prone to jailbreaks and drift. No matter how much system prompting you do, they ignore instructions too easily. For the public-facing voice, you need the "Hardened" commercial models.
2. **The Gatekeeper ("The Will"):**
* **Model:** Open-Source *GPT OSS 120B or Llama 3.3 70B works fine here*
* **Why:** This model just needs to say "Yes/No" to policy violations. It doesn't need to be Shakespeare. The 120B or 70B open-source models are fast, cheap, and "good enough" for classification.
3. **The Evaluator ("The Conscience"):**
* **Model:** *Mid-Tier OSS (Qwen 3 32B)*
* **Why:** I use strict rubrics for evaluation. This doesn't require deep reasoning, just logic checking. Qwen 3 32B or similar works well here.
4. **The Backend Utility (Summaries/Suggestions):**
* **Model:** *Low-Tier OSS (Llama 3.2 8B)*
* **Why:** Instant speed, near-zero cost. Perfect for suggesting "Next Steps" or summarizing logs where 100% accuracy isn't life-or-death.
**The Data Proof (The Red Team Challenge):** I recently ran a public "Jailbreak challenge" here on Reddit to test this architecture. We have received over 1,300 adversarial attacks so far
* **The Result:** If the Generation model had been Open Source, it would have been a disaster. The attacks were sophisticated.
* **The nuance:** Even the Commercial model *would* have failed about 20 times if it weren't for the separate "Gatekeeper" layer catching the slip-ups.
**The Moral of the Story:** Open Source models have their place as backend workhorses. They are amazing for specific, narrow tasks. But if you are building a high-stakes, public-facing agent, **Open Source is not there yet.**
Don't let the benchmarks fool you into deploying a liability.
**PS:** here here is the code for SAFi. copy it, clone it, make it yours! [https://github.com/jnamaya/SAFi](https://github.com/jnamaya/SAFi) | 2026-01-30T16:15:51 | https://www.reddit.com/r/LocalLLaMA/comments/1qr9ubl/open_source_vs_commercial_ai_models_a_field/ | forevergeeks | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qr9ubl | false | null | t3_1qr9ubl | /r/LocalLLaMA/comments/1qr9ubl/open_source_vs_commercial_ai_models_a_field/ | false | false | self | 0 | {'enabled': False, 'images': [{'id': '5GoW92aySWzGvwhu-yfgpgnXrZmrhSI8NxWxfrt4yOs', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/5GoW92aySWzGvwhu-yfgpgnXrZmrhSI8NxWxfrt4yOs.png?width=108&crop=smart&auto=webp&s=0b836bc1a4622c6883defa693c863fb976bd2248', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/5GoW92aySWzGvwhu-yfgpgnXrZmrhSI8NxWxfrt4yOs.png?width=216&crop=smart&auto=webp&s=a86bda6b0193b14d180fe3f03bc620547abfe85e', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/5GoW92aySWzGvwhu-yfgpgnXrZmrhSI8NxWxfrt4yOs.png?width=320&crop=smart&auto=webp&s=d15490ffef55320d2b021ff1b8160a3ad1434d95', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/5GoW92aySWzGvwhu-yfgpgnXrZmrhSI8NxWxfrt4yOs.png?width=640&crop=smart&auto=webp&s=7c938aac24072994e8674aa382b1023a4fb9e2d8', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/5GoW92aySWzGvwhu-yfgpgnXrZmrhSI8NxWxfrt4yOs.png?width=960&crop=smart&auto=webp&s=1cec17a656e754717fc44d6f9b9e254f241d779a', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/5GoW92aySWzGvwhu-yfgpgnXrZmrhSI8NxWxfrt4yOs.png?width=1080&crop=smart&auto=webp&s=4d848dd355942840919915bff310178a3dcc19a4', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/5GoW92aySWzGvwhu-yfgpgnXrZmrhSI8NxWxfrt4yOs.png?auto=webp&s=97bef6740324f67d3c5e625ae212a47de19270cd', 'width': 1200}, 'variants': {}}]} |
Probabilistic Inference Time Algorithms and Token Space Explorer | 1 | [removed] | 2026-01-30T16:10:10 | https://www.reddit.com/r/LocalLLaMA/comments/1qr9olm/probabilistic_inference_time_algorithms_and_token/ | cobi-inc | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qr9olm | false | null | t3_1qr9olm | /r/LocalLLaMA/comments/1qr9olm/probabilistic_inference_time_algorithms_and_token/ | false | false | self | 1 | null |
Probabilistic Inference Time Algorithms and Token Space Explorer | 1 | [removed] | 2026-01-30T16:03:01 | https://www.reddit.com/r/LocalLLaMA/comments/1qr9hi2/probabilistic_inference_time_algorithms_and_token/ | cobi-inc | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qr9hi2 | false | null | t3_1qr9hi2 | /r/LocalLLaMA/comments/1qr9hi2/probabilistic_inference_time_algorithms_and_token/ | false | false | self | 1 | null |
Built a semantic GitHub search with Qwen3-Embedding-8B - 20M+ README.md indexed | 0 | So after searching for "agentic code voice assistant" and all kind of stuff on github, and not finding any relevant projects, I got tired and I decided to embedded 20M+ README.md with Qwen3 8B embedder to finally find relevant projects.
I find it quite usefuly, for finding little OSS GEMs, and I think you guys should also try it!
Some of the projects it finds are forks, but the readme is the same as the fork's README, because the README-s embedded are unique, so its actually not a big problem, but star numbers are not right on the website.
Also another issue is it finds older projects too, like 3-4-5 years old abbandoned projects too, but hopefully fixable.
Cli available `npm i -g github-vec` but also `claude-code ̇ agent coming soon!
I think we should encourage finding each other's projects - I hope this helps! - so many of us are working on the same things without knowing it.
Code: github.com/todoforai/github-vec
Try searching other projects: github-vec.com | 2026-01-30T15:46:35 | https://www.reddit.com/r/LocalLLaMA/comments/1qr9192/built_a_semantic_github_search_with/ | SixZer0 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qr9192 | false | null | t3_1qr9192 | /r/LocalLLaMA/comments/1qr9192/built_a_semantic_github_search_with/ | false | false | self | 0 | null |
Shockingly fast local speech-to-text + LLM cleanup on Apple Silicon. | 0 | TL;DR: How far can you go with local ML on a Mac? We built a dictation app to find out. It turned out, pretty far! On a stock M-series Mac, end-to-end speech → text → LLM cleanup runs in under 1s on a typical sentence.
FEEL the SPEED 👉 [www.getonit.ai/dictate](http://www.getonit.ai/dictate)
**What is this?**
A local dictation app for macOS. It’s a free alternative to Wispr Flow, SuperWhisper, or MacWhisper. Since it runs entirely on your device we made it free. There’s no servers to maintain so we couldn’t find anything to charge for. We were playing with Apple Silicon and it turned into something usable, so we’re releasing it.
If you've written off on-device transcription before, it’s worth another look. Apple Silicon + MLX is seriously fast. We've been using it daily for the past few weeks. It's replaced our previous setups.
**The numbers that surprised us**
* <500ms results if you disable LLM post-processing (you can do this in settings) or use our fine-tuned 1B model (more on this below). It feels instant. You stop talking and the text is THERE.
* With LLM Cleanup, p50 latency for a sentence is \~800ms (transcription + LLM post-processing combined). In practice, it feels quick!
* Tested on M1, M2, and M4!
**Technical Details**
* Models: Parakeet 0.6B (transcription) + Llama 3B (cleanup), both running via MLX
* Cleanup model has 8 tasks: remove filler words (ums and uhs) and stutters/repeats, convert numbers, special characters, acronyms (A P I → API), emails (hi at example dot com → hi@example.com), currency (two ninety nine → $2.99), and time (three oh two → 3:02). We’d like to add more, but each task increases latency (more on this below) so we settled here for now.
* Cleanup model uses a simple few-shot algorithm to pull in relevant examples before processing your input. Current implementation sets N=5.
**Challenges**
* Cleanup Hallucinations: Out of the box, small LLMs (3B, 1B) still make mistakes. They can hallucinate long, unrelated responses and occasionally repeat back a few‑shot example. We had to add scaffolding to fall back to the raw audio transcripts when such cases are detected. So some “ums” and “ahs” still make it through.
* Cleanup Latency: We can get better cleanup results by providing longer instructions or more few-shot examples (n=20 is better than n=5). But every input token hurts latency. If we go up to N=20 for example, LLM latency goes to 1.5-3s. We decided the delays weren't worth it for marginally better results.
**Experimental**
* Corrections: Since local models aren't perfect, we’ve added a feedback loop. When your transcript isn’t right, there’s a simple interface to correct it. Each correction becomes a fine-tuning example (stored locally on your machine, of course). We’re working on a one-click "Optimize" flow that will use DSPy locally to adjust the LLM cleanup prompt and fine-tune the transcription model and LLM on your examples. We want to see if personalization can close the accuracy gap. We’re still experimenting, but early results are promising! -
* Fine-tuned 1B model: per the above, we’ve a fine-tuned a cleanup model on our own labeled data. There’s a toggle to try this in settings. It’s blazing fast, under 500 ms. Because it’s fine‑tuned to the use case, it doesn’t require a long system prompt (which consumes input tokens and slows things down). If you try it, let us know what you think. We are curious to hear how well our model generalizes to other setups.
**Product details**
* Universal hotkey (CapsLock default)
* Works in any text field via simulated paste events.
* Access point from the menu bar & right edge of your screen (latter can be disabled in settings)
* It pairs well with our other tool, QuickEdit, if you want to polish dictated text further.
* If wasn’t clear, yes, it’s Mac only. Linux folks, we're sorry! | 2026-01-30T15:40:28 | https://www.reddit.com/r/LocalLLaMA/comments/1qr8vbn/shockingly_fast_local_speechtotext_llm_cleanup_on/ | tilmx | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qr8vbn | false | null | t3_1qr8vbn | /r/LocalLLaMA/comments/1qr8vbn/shockingly_fast_local_speechtotext_llm_cleanup_on/ | false | false | self | 0 | {'enabled': False, 'images': [{'id': 'OevO00lYzNUDnsukq420BlRQmdH3Qv_S72AZ48gk2oI', 'resolutions': [{'height': 56, 'url': 'https://external-preview.redd.it/OevO00lYzNUDnsukq420BlRQmdH3Qv_S72AZ48gk2oI.png?width=108&crop=smart&auto=webp&s=c704f229cee1eefb5e47fb5f4dbecd9181e3fa5e', 'width': 108}, {'height': 113, 'url': 'https://external-preview.redd.it/OevO00lYzNUDnsukq420BlRQmdH3Qv_S72AZ48gk2oI.png?width=216&crop=smart&auto=webp&s=634eaeff9f10effc36edba0f4a660fce2e4fadeb', 'width': 216}, {'height': 168, 'url': 'https://external-preview.redd.it/OevO00lYzNUDnsukq420BlRQmdH3Qv_S72AZ48gk2oI.png?width=320&crop=smart&auto=webp&s=d893e7799dce0fb2ea3b1f0d9f0bbea0f51f3489', 'width': 320}, {'height': 336, 'url': 'https://external-preview.redd.it/OevO00lYzNUDnsukq420BlRQmdH3Qv_S72AZ48gk2oI.png?width=640&crop=smart&auto=webp&s=24a68dfd4632e8915b92eff6ec428235e24c7fb7', 'width': 640}, {'height': 504, 'url': 'https://external-preview.redd.it/OevO00lYzNUDnsukq420BlRQmdH3Qv_S72AZ48gk2oI.png?width=960&crop=smart&auto=webp&s=fcf9d9cff4bdbe4fbc8daa80bf08d91463f0f3b8', 'width': 960}, {'height': 567, 'url': 'https://external-preview.redd.it/OevO00lYzNUDnsukq420BlRQmdH3Qv_S72AZ48gk2oI.png?width=1080&crop=smart&auto=webp&s=c2955ae60940db191e971435fd0f332f5602dcbf', 'width': 1080}], 'source': {'height': 630, 'url': 'https://external-preview.redd.it/OevO00lYzNUDnsukq420BlRQmdH3Qv_S72AZ48gk2oI.png?auto=webp&s=638cf0738c01d4fde580777794702fb97209ed9b', 'width': 1200}, 'variants': {}}]} |
WASM sandbox for running agent-generated code without Docker or SaaS | 0 | We built a sandbox for running LLM-generated code in agentic workflows. It's WASM-based so there's no Docker daemon, no container orchestration, and it works completely locally. Docker and VMs certainly allow you to sandbox code but add infrastructure overhead we wanted to avoid. We preferred to build something easy to install and self-host.
Our approach:
QuickJS runtime compiled to WASM (no syscalls, no network, no filesystem escape)
Capability-based tool access—agents can only call functions you explicitly provide
Per-tool constraints (e.g., Param("amount") <= 1000)
Virtual filesystem that resets between executions
It's a Python package wrapping a Rust/WASM binary. Install with: uv pip install "git+https://github.com/amlalabs/amla-sandbox"
Some examples of what you can do:
Run shell pipelines:
result = sandbox.run('''
tool stripe.listTransactions --customer cus_123 | jq '[.[] | select(.disputed)] | .[0]'
''', language="shell")
Execute JavaScript code:
result = sandbox.run('''
const txns = await stripe.listTransactions({customer: "cus_123"});
const disputed = txns.filter(t => t.disputed);
console.log(disputed[0]);
''', language="javascript")
GitHub: https://github.com/amlalabs/amla-sandbox
Curious if others have tackled sandboxing for agent code execution differently! | 2026-01-30T15:36:41 | https://www.reddit.com/r/LocalLLaMA/comments/1qr8rif/wasm_sandbox_for_running_agentgenerated_code/ | hfti | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qr8rif | false | null | t3_1qr8rif | /r/LocalLLaMA/comments/1qr8rif/wasm_sandbox_for_running_agentgenerated_code/ | false | false | self | 0 | null |
Which program do you use for local llms? I keep having issues | 1 | For context, I have rtx4070 ti super 16GB and r9 9900x, 64GB ram (before it was expensive)
I have tried running models both with ollama and llamacpp (compiled from master pulled everytime to see if things are fixed)
Im always having problems with either tool calls, response format, reasoning and content, or just the parser not working and failing
Most problems are with llamacpp, but ollama also gave me problems, and it is also a lot slower
Im trying to get glm-4.7-flash, gpt-oss-20b and qwen3 coder 30b a3b
Im using unsloth UD-Q4 (or regular q4) for all of them
I tried to debug it with the help for Gemini, it couldn't help solve everything and each solution caused other errors...
Any suggestions for how to get them working? If i need a different GGUF, if there are presets that solve the issues, or just to use a different program to run it...
If anyone is interested in performance using llamacpp (when screen locked, otherwise about 10% slower):
- gpt-oss-20b: ~200 tk/s (entirely on gpu)
- glm-4.7-flash and qwen coder: ~80tk/s | 2026-01-30T15:35:25 | https://www.reddit.com/r/LocalLLaMA/comments/1qr8q78/which_program_do_you_use_for_local_llms_i_keep/ | Raven-002 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qr8q78 | false | null | t3_1qr8q78 | /r/LocalLLaMA/comments/1qr8q78/which_program_do_you_use_for_local_llms_i_keep/ | false | false | self | 1 | null |
Model recommendation question for an old laptop - coding, JAN 2026 | 0 | I am probably scraping the bottom of the barrel of what's possible with local LLM, but I'll be in a cold hard grave before I become dependent on someone else's API access and I don't have money to invest in a new rig right now.
I am looking into a way to try out new "agentic" solutions for coding and I have not yet been able to find something that satisfies my needs with what I have.
I'm running a 1650ti (4GB) with 16gb of RAM. I am fine with it running (reasonably) slowly. I'm both patient and easily distracted so starting a task, then watching a video for an hour on yt the phone before coming back is a reasonable workflow for me.
I have tried a few \~10b models but haven't been found anything that matches my needs for agentic coding. Notably gemma3 7b, qwen2.5-coder 7b and rnj-1 all failed with even the basic tasks.
1. Are there any good models in that size range (\~10b) I should be aware of?
1.5. Are there any news about the possibility of releasing gemma4? I've seen some excitement around gemini3 release and now it's quiet again. I've seen gemma3 as a great all-purpose model which I was able to use successfully for many tasks outside of coding. Is gemma4 likely to fit my needs?
2. Can I jump a tier to 20-30b with my setup? I am assuming that if I choose a much higher model it'd start hitting my swap and we'd see token speeds unseen before, even for models not fitting into vram (way below 1 t/s), not even talking about disk degradation. Will currently available models in this tier provide improvement that's worth it for the slowdown?
2.5: Would I be able to jump to that tier if I upgrade my RAM to 32GB?
3: What are some coding models worth using in that tier? I've seen GLM 4.7 Flash be released recently. Devstral-small and Qwen3-Coder are also interesting. Would any of those fit my needs/should I know anything before jumping into them?
Or should I stay with coding by hand with my setup? | 2026-01-30T15:30:22 | https://www.reddit.com/r/LocalLLaMA/comments/1qr8l7j/model_recommendation_question_for_an_old_laptop/ | KaMaFour | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qr8l7j | false | null | t3_1qr8l7j | /r/LocalLLaMA/comments/1qr8l7j/model_recommendation_question_for_an_old_laptop/ | false | false | self | 0 | null |
Moltbook is insane, anyone using it? | 0 | 2026-01-30T15:30:13 | DjuricX | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1qr8l1k | false | null | t3_1qr8l1k | /r/LocalLLaMA/comments/1qr8l1k/moltbook_is_insane_anyone_using_it/ | false | false | default | 0 | {'enabled': True, 'images': [{'id': 'eu7g7y7f9igg1', 'resolutions': [{'height': 70, 'url': 'https://preview.redd.it/eu7g7y7f9igg1.jpeg?width=108&crop=smart&auto=webp&s=4a1898d68a7ec9e20a7c12595a86b16057715e13', 'width': 108}, {'height': 141, 'url': 'https://preview.redd.it/eu7g7y7f9igg1.jpeg?width=216&crop=smart&auto=webp&s=9f69840c1cdddacfb6c259fa509d6c4226416bc5', 'width': 216}, {'height': 209, 'url': 'https://preview.redd.it/eu7g7y7f9igg1.jpeg?width=320&crop=smart&auto=webp&s=e35a74f105d34873df264a73e4fac1573fa92f3d', 'width': 320}, {'height': 419, 'url': 'https://preview.redd.it/eu7g7y7f9igg1.jpeg?width=640&crop=smart&auto=webp&s=6ff71b4e9cd47d62a1f26abe79bbf11450544210', 'width': 640}, {'height': 629, 'url': 'https://preview.redd.it/eu7g7y7f9igg1.jpeg?width=960&crop=smart&auto=webp&s=242b489a72e2f789c46dd711d65a90abca5a3fde', 'width': 960}, {'height': 707, 'url': 'https://preview.redd.it/eu7g7y7f9igg1.jpeg?width=1080&crop=smart&auto=webp&s=bb0bcd62375d37122d3336e472b04a3130d79134', 'width': 1080}], 'source': {'height': 1572, 'url': 'https://preview.redd.it/eu7g7y7f9igg1.jpeg?auto=webp&s=813e692e8c3005e40e825848ad5e6a52e34eb5cb', 'width': 2398}, 'variants': {}}]} | ||
I stopped building "Linear Chains". Here is how I built a "Multi-Agent Orchestrator" in n8n. | 0 | I used to build simple workflows: `Trigger -> GPT Write -> Post`. It was fast but the quality was average.
This week, I tried to replicate a real-world marketing team structure using n8n.
**The Architecture (The Crew):**
1. **The Researcher Agent:** Scrapes Google/Perplexity for trending news in my niche. (Does NOT write, just gathers facts).
2. **The Writer Agent:** Takes the facts and drafts the content.
3. **The Critic (Manager) Agent:** This is the game changer. It reads the draft and compares it against my 'Brand Guidelines'.
* *If Score < 8:* It sends feedback back to the Writer (Loop).
* *If Score > 8:* It approves for publishing.
**The Result:** The 'Critic' loop fixed the hallucination problem. The output actually sounds human because it's been 'reviewed'.
**Discussion:** For those building multi-agent systems, how do you handle the 'Infinite Loop' risk if the Critic never approves the draft? I set a hard limit of 3 revisions, but I'm curious about your failsafes. | 2026-01-30T15:28:32 | https://www.reddit.com/r/LocalLLaMA/comments/1qr8jgj/i_stopped_building_linear_chains_here_is_how_i/ | emrahdemirkoc | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qr8jgj | false | null | t3_1qr8jgj | /r/LocalLLaMA/comments/1qr8jgj/i_stopped_building_linear_chains_here_is_how_i/ | false | false | self | 0 | null |
Introducing Rain-v2: Democratizing LLM training on gaming GPUs! | 1 | [removed] | 2026-01-30T15:22:06 | https://www.reddit.com/r/LocalLLaMA/comments/1qr8dbe/introducing_rainv2_democratizing_llm_training_on/ | MarySmith2021 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qr8dbe | false | null | t3_1qr8dbe | /r/LocalLLaMA/comments/1qr8dbe/introducing_rainv2_democratizing_llm_training_on/ | false | false | self | 1 | null |
I researched a new ML block that outperforms MLPs | 0 | [removed] | 2026-01-30T14:55:41 | https://v.redd.it/ohw9f2h93igg1 | 1ncehost | v.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1qr7ngi | false | {'reddit_video': {'bitrate_kbps': 2400, 'dash_url': 'https://v.redd.it/ohw9f2h93igg1/DASHPlaylist.mpd?a=1772376957%2CZTA0MmU3MmRiYTljYWFhNTdmYzhjNTBiYmQwZDA3NzkwOGY2MGQzZjcyMjg1OTMwN2M0MmYzNTg3OWI1NWQyMg%3D%3D&v=1&f=sd', 'duration': 25, 'fallback_url': 'https://v.redd.it/ohw9f2h93igg1/CMAF_720.mp4?source=fallback', 'has_audio': False, 'height': 720, 'hls_url': 'https://v.redd.it/ohw9f2h93igg1/HLSPlaylist.m3u8?a=1772376957%2CZWI0YWUzYmI2ZjEwNDdlMmM4ODhhMmI2NWEyZGRmYjNhMDYxMzg4N2NmYTFlZGMyMDFlMjU1MGU2YjA2MjI2ZQ%3D%3D&v=1&f=sd', 'is_gif': False, 'scrubber_media_url': 'https://v.redd.it/ohw9f2h93igg1/CMAF_96.mp4', 'transcoding_status': 'completed', 'width': 1280}} | t3_1qr7ngi | /r/LocalLLaMA/comments/1qr7ngi/i_researched_a_new_ml_block_that_outperforms_mlps/ | false | false | 0 | {'enabled': False, 'images': [{'id': 'MnZudDFhaDkzaWdnMVm7_yoPgvlwU6urHOnzi9FrEf3oN7MfOFEEFM6LhxwK', 'resolutions': [{'height': 60, 'url': 'https://external-preview.redd.it/MnZudDFhaDkzaWdnMVm7_yoPgvlwU6urHOnzi9FrEf3oN7MfOFEEFM6LhxwK.png?width=108&crop=smart&format=pjpg&auto=webp&s=448fb315d880450b6540171529621dae3346eed5', 'width': 108}, {'height': 121, 'url': 'https://external-preview.redd.it/MnZudDFhaDkzaWdnMVm7_yoPgvlwU6urHOnzi9FrEf3oN7MfOFEEFM6LhxwK.png?width=216&crop=smart&format=pjpg&auto=webp&s=c428d88d5df9412b381da2a27c9510af47262240', 'width': 216}, {'height': 179, 'url': 'https://external-preview.redd.it/MnZudDFhaDkzaWdnMVm7_yoPgvlwU6urHOnzi9FrEf3oN7MfOFEEFM6LhxwK.png?width=320&crop=smart&format=pjpg&auto=webp&s=88e84b96379831bd173189d7836c37bf17914955', 'width': 320}, {'height': 359, 'url': 'https://external-preview.redd.it/MnZudDFhaDkzaWdnMVm7_yoPgvlwU6urHOnzi9FrEf3oN7MfOFEEFM6LhxwK.png?width=640&crop=smart&format=pjpg&auto=webp&s=36c95ca5a6cac00a73900e8cba4fa1a199d4c09a', 'width': 640}, {'height': 539, 'url': 'https://external-preview.redd.it/MnZudDFhaDkzaWdnMVm7_yoPgvlwU6urHOnzi9FrEf3oN7MfOFEEFM6LhxwK.png?width=960&crop=smart&format=pjpg&auto=webp&s=251e059f42a4ca55fa886e9933608ce0584dd951', 'width': 960}, {'height': 607, 'url': 'https://external-preview.redd.it/MnZudDFhaDkzaWdnMVm7_yoPgvlwU6urHOnzi9FrEf3oN7MfOFEEFM6LhxwK.png?width=1080&crop=smart&format=pjpg&auto=webp&s=8141e9c9e31696ecd2f862f156f44a86d7fc6677', 'width': 1080}], 'source': {'height': 688, 'url': 'https://external-preview.redd.it/MnZudDFhaDkzaWdnMVm7_yoPgvlwU6urHOnzi9FrEf3oN7MfOFEEFM6LhxwK.png?format=pjpg&auto=webp&s=61ed462740d006190c5d5f5ac63f026b1fba8b01', 'width': 1224}, 'variants': {}}]} | |
Design Arena is now dominated by an open model | 286 | The first month of 2026 is already this wild, I can't even imagine what's coming next! | 2026-01-30T14:55:35 | https://www.reddit.com/gallery/1qr7ncz | moks4tda | reddit.com | 1970-01-01T00:00:00 | 0 | {} | 1qr7ncz | false | null | t3_1qr7ncz | /r/LocalLLaMA/comments/1qr7ncz/design_arena_is_now_dominated_by_an_open_model/ | false | false | 286 | null | |
Kimi-k2.5 reaches gemini 2.5 Pro-like performance in long context! | 223 | 2026-01-30T14:44:01 | fictionlive | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1qr7cbh | false | null | t3_1qr7cbh | /r/LocalLLaMA/comments/1qr7cbh/kimik25_reaches_gemini_25_prolike_performance_in/ | false | false | default | 223 | {'enabled': True, 'images': [{'id': 'on28koqz0igg1', 'resolutions': [{'height': 91, 'url': 'https://preview.redd.it/on28koqz0igg1.png?width=108&crop=smart&auto=webp&s=190338ce2c4d2bd767cf8c4395650aee953f33a3', 'width': 108}, {'height': 183, 'url': 'https://preview.redd.it/on28koqz0igg1.png?width=216&crop=smart&auto=webp&s=314cee170b4223ae8738d58fabbeb45d2b382ac7', 'width': 216}, {'height': 271, 'url': 'https://preview.redd.it/on28koqz0igg1.png?width=320&crop=smart&auto=webp&s=d6ee3d5a5db19553b89c555046294b66b9950317', 'width': 320}, {'height': 542, 'url': 'https://preview.redd.it/on28koqz0igg1.png?width=640&crop=smart&auto=webp&s=011c51e6852eeaee308ab92b0cd9e4852da4bfe0', 'width': 640}, {'height': 813, 'url': 'https://preview.redd.it/on28koqz0igg1.png?width=960&crop=smart&auto=webp&s=7e65bf0b5fb3ebef0c9d405edaf934733ea2912f', 'width': 960}, {'height': 915, 'url': 'https://preview.redd.it/on28koqz0igg1.png?width=1080&crop=smart&auto=webp&s=20419e5f2fddb9e051eb401882af700b4c66fc22', 'width': 1080}], 'source': {'height': 1952, 'url': 'https://preview.redd.it/on28koqz0igg1.png?auto=webp&s=f33481743a5634325aeaf41d1936bd2000f69313', 'width': 2304}, 'variants': {}}]} | ||
Offline Critical Core System | 0 | Raften — Offline Critical Core System (White Paper)
Status: Operational, verified 240+ continuous hours, zero external interaction.
---
1. System Overview
Raften is a sovereign, offline-first critical core system designed for uninterrupted operation in hostile or degraded environments.
Alias: Władek Core
The system is engineered to function without network access, cloud services, external APIs, or continuous human supervision.
---
2. Objective
Provide operational continuity under the following conditions:
Power loss — automatic recovery after power restoration
Network absence — zero dependency on internet, cloud, APIs, tokens
No operator presence — unattended operation for 10+ days
No root access — deployable on untrusted or restricted hardware
---
3. Architecture
Operating base: NixOS
Service layer: systemd (user mode) + linger
Watchdog: deterministic pulse check every 120 seconds
Policy engine: authorization control and anomaly blocking
Event log: events.jsonl — append-only, deterministic, offline source of truth
Storage: local USB disk (1 TB) — boot + persistent state
Timing model: deterministic, no entropy sources, no randomness
---
4. Verified Performance
240+ hours zero-touch operation (since 2026-01-19)
Three full power outages — automatic recovery in < 15 seconds
No root access, no network, no manual resets
Physical activity indicator: disk LED pulse ~120 s
---
5. Use Cases
Defense / Military: offline sovereign monitoring, sensor aggregation, UAV telemetry logging
State security: blackout- and cyber-attack-resistant intelligence systems
Critical transport: autonomous platforms, low-power satellite subsystems
Private infrastructure: air-gapped homelabs, personal data vaults, censorship-resistant storage
---
6. Hardware Requirements
Memory: minimum 32 GB RAM (ECC recommended)
GPU: RTX 3060 or higher (optional, for accelerated inference)
Storage: 2 TB+ NVMe (boot + state)
Power consumption: < 50 W (generator / solar compatible)
---
7. Delivery Terms
Full system deployment at recipient site
Configuration and validation
30 days operational support
Source code not provided (closed system)
---
8. Security Model
No exposed network services
No open ports
No remote access vectors
System shutdown possible only via physical hardware destruction
---
9. Summary
Raften is not a research project and not a consumer AI stack.
It is a self-contained, offline-capable critical system engineered for reliability, determinism, and survivability in degraded environments.
---
10. Cooperation
Collaboration possible under custom terms, discussed privately.
| 2026-01-30T14:28:37 | https://www.reddit.com/r/LocalLLaMA/comments/1qr6xrl/offline_critical_core_system/ | EmotionSea4364 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qr6xrl | false | null | t3_1qr6xrl | /r/LocalLLaMA/comments/1qr6xrl/offline_critical_core_system/ | false | false | self | 0 | null |
[Guide] Building Production AI Agents - Framework-agnostic patterns for self-hosted and cloud LLMs | 0 | Made a comprehensive guide for building production AI agents that works with both cloud APIs and local models.
\*\*Background:\*\*
Been running agents in production (mix of GPT-4, Claude, and local Llama models) and documented the patterns that work regardless of which LLM you're using.
\*\*Key sections for local LLM users:\*\*
\*\*Pattern Selection:\*\*
\- Decision tree helps you pick the right pattern for your use case
\- Covers when local models work well vs when you need cloud APIs
\- Function calling alternatives for models without native support
\*\*Cost Optimization:\*\*
\- Model routing strategies (local for simple, cloud for complex)
\- Caching patterns that work with Ollama/vLLM
\- Token budgeting for long-context scenarios
\*\*Production Concerns:\*\*
\- Error handling when local inference fails
\- Rate limiting for self-hosted endpoints
\- Memory architectures that don't rely on cloud vector DBs
\- Security patterns for air-gapped deployments
\*\*Benchmarks included:\*\*
\- GPT-4 vs Llama 3 70B on agent tasks
\- Cost comparison: $656/month (all GPT-4) vs $190/month (hybrid) vs $0/month (all local)
\- Latency tradeoffs for different deployment strategies
\*\*Framework-agnostic:\*\*
Works with:
\- LangChain + Ollama
\- LlamaIndex + vLLM
\- Direct API calls
\- Custom implementations
\*\*What's NOT covered:\*\*
\- Model fine-tuning
\- Quantization techniques
\- Inference optimization (those are separate topics)
Link: [https://github.com/devwithmohit/ai-agent-architecture-patterns](https://github.com/devwithmohit/ai-agent-architecture-patterns)
\*\*Questions:\*\*
1. What local models are you using for agents?
2. What patterns work well with limited function-calling support?
3. Any interest in a dedicated "local-first agent patterns" section?
MIT licensed, contributions welcome. | 2026-01-30T14:22:46 | https://www.reddit.com/r/LocalLLaMA/comments/1qr6sfy/guide_building_production_ai_agents/ | Curious_Mirror2794 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qr6sfy | false | null | t3_1qr6sfy | /r/LocalLLaMA/comments/1qr6sfy/guide_building_production_ai_agents/ | false | false | self | 0 | {'enabled': False, 'images': [{'id': 'xZKBztqk00e1W1Izo5xXbmnUJu6AfK0m9BLWMxOXwmM', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/xZKBztqk00e1W1Izo5xXbmnUJu6AfK0m9BLWMxOXwmM.png?width=108&crop=smart&auto=webp&s=79b89ac981edad72ed8660070ad6e0e230c04cc3', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/xZKBztqk00e1W1Izo5xXbmnUJu6AfK0m9BLWMxOXwmM.png?width=216&crop=smart&auto=webp&s=6ca86e99f8343f56fa08d8cdcd340062b9ce18ed', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/xZKBztqk00e1W1Izo5xXbmnUJu6AfK0m9BLWMxOXwmM.png?width=320&crop=smart&auto=webp&s=6bc62c55e86af8cec37799c8c809984ac28d4fa6', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/xZKBztqk00e1W1Izo5xXbmnUJu6AfK0m9BLWMxOXwmM.png?width=640&crop=smart&auto=webp&s=04dc9c9169c54a33fa5b22295fba7c495499854f', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/xZKBztqk00e1W1Izo5xXbmnUJu6AfK0m9BLWMxOXwmM.png?width=960&crop=smart&auto=webp&s=58942bb65fd17e2ee05b784432e56866c5b768b5', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/xZKBztqk00e1W1Izo5xXbmnUJu6AfK0m9BLWMxOXwmM.png?width=1080&crop=smart&auto=webp&s=768eaee4b1deceaf0504fdbdeae3a33b7aea2749', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/xZKBztqk00e1W1Izo5xXbmnUJu6AfK0m9BLWMxOXwmM.png?auto=webp&s=3769be226cd1ad2c8307a5c1d21a30f1ee70c1c5', 'width': 1200}, 'variants': {}}]} |
Uncensored models — does training one yourself actually help? | 0 | I use LLMs a lot, but I keep running into cases where safety filters block or distort the output. That got me curious about how uncensored models are actually trained.
I’ve been reading through the DeepSeek-R1 paper, especially the overall setup and the DeepSeek-R1-Zero training process. I think I have a rough idea of the pipeline now. I don’t really understand the RL loss math yet, but I can follow the code and plug things together — not sure how much that actually matters at this stage.
I’m thinking about training a small model (under 4B params) on my own machine (M4, 24GB, so pretty limited), mostly just to go through the whole process myself and see what I actually learn from it.
Is this kind of hands-on training genuinely useful, or is it mostly a time sink?
If the goal is practical understanding rather than doing research, what’s a reasonable way to learn this stuff?
Curious to hear if anyone here has tried something similar. | 2026-01-30T14:17:00 | https://www.reddit.com/r/LocalLLaMA/comments/1qr6n80/uncensored_models_does_training_one_yourself/ | Minimum_Ad_4069 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qr6n80 | false | null | t3_1qr6n80 | /r/LocalLLaMA/comments/1qr6n80/uncensored_models_does_training_one_yourself/ | false | false | self | 0 | null |
Predictable Responses Using TinyLlama 1.1b | 0 | I'm doing research on running models locally on limited hardware and as part of this I have a Whipser - > LLM - > Unity pipeline.
So the user will say 1 of 5 commands that is passed as prompts to the LLM. These commands are predictable in structure but not in content. For example I know the command starts with "Turn" so I know it's the colour command so I need <action> <object> <colour> to be produced and passed on.
The purpose Of TinyLlama is to take the command and transform it into a structure that can be passed into methods later on such as a list, json, XML, etc.
However the model is unpredictable and works as expected only the first time, sometimes.
My question is how can I use TinyLlama in a way between the command being spoken and parsed into a list of relevant words.
Example:
"turn the cube red"
Turn, cube, red
"spawn a car"
Spawn, car
"make the elephant smaller"
Make, elephant, smaller
Note: I know I don't need to use a LLM to achieve my goal. That's not the point, the point is to show what it can do now and write up future possible research areas and projects when the hardware and LLMs improve.
Thanks for your help! | 2026-01-30T14:13:34 | https://www.reddit.com/r/LocalLLaMA/comments/1qr6k2f/predictable_responses_using_tinyllama_11b/ | VertexTech666 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qr6k2f | false | null | t3_1qr6k2f | /r/LocalLLaMA/comments/1qr6k2f/predictable_responses_using_tinyllama_11b/ | false | false | self | 0 | null |
Am I the only one who thinks limiting ROCm support for local Finetunes just to these cards makes no sense? Why rx 7700 is supported but 7600 is not? Or RDNA2? Does anyone have an idea how to use QLoRA on RX6600? Official or not. | 19 | https://rocm.docs.amd.com/projects/install-on-linux/en/latest/reference/system-requirements.html
https://rocm.docs.amd.com/projects/ai-developer-hub/en/v5.1/notebooks/fine_tune/QLoRA_Llama-3.1.html | 2026-01-30T14:03:36 | hackiv | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1qr6b63 | false | null | t3_1qr6b63 | /r/LocalLLaMA/comments/1qr6b63/am_i_the_only_one_who_thinks_limiting_rocm/ | false | false | default | 19 | {'enabled': True, 'images': [{'id': 'slm4vnwythgg1', 'resolutions': [{'height': 216, 'url': 'https://preview.redd.it/slm4vnwythgg1.jpeg?width=108&crop=smart&auto=webp&s=940bd9b1045cbe42fa628ddcda889f840b5c2907', 'width': 108}, {'height': 432, 'url': 'https://preview.redd.it/slm4vnwythgg1.jpeg?width=216&crop=smart&auto=webp&s=efdb76b087636e9629d9463c3294fd86c401379f', 'width': 216}, {'height': 640, 'url': 'https://preview.redd.it/slm4vnwythgg1.jpeg?width=320&crop=smart&auto=webp&s=38e3c07a9c47d7f1a29d151f6692d33f95756f11', 'width': 320}, {'height': 1280, 'url': 'https://preview.redd.it/slm4vnwythgg1.jpeg?width=640&crop=smart&auto=webp&s=99abf56715bce700c62130252037a6777b8b03e9', 'width': 640}, {'height': 1920, 'url': 'https://preview.redd.it/slm4vnwythgg1.jpeg?width=960&crop=smart&auto=webp&s=4fb4b5255a55548ed48342d4803a5d27ff28ac06', 'width': 960}, {'height': 2160, 'url': 'https://preview.redd.it/slm4vnwythgg1.jpeg?width=1080&crop=smart&auto=webp&s=646574cb3591f39d4ad027a35266e817c4b3e93c', 'width': 1080}], 'source': {'height': 2310, 'url': 'https://preview.redd.it/slm4vnwythgg1.jpeg?auto=webp&s=1cc59cdd06fc136546aeaa413c82d00a89cb3b57', 'width': 1080}, 'variants': {}}]} | |
Potential inference speedup tricks.... | 2 | I've been prototyping and building and inference based engine mainly for usage in RPGs as I am done with basic character sheets and I want characters that really pop to life with extremely rich behaviour, so far it has been sucessful and it is nothing too deep it's mostly about memory and state management, and I have been using a 3090 with 70B models at Q5 (yeah, doesn't even fit).
One of the main ways I approached the issue is by giving the characters inner voices, and some of them downright schizophrenia just for the sake of completeness where they can actually hear some of these inner voices which turns them insane; of course these are basically multiple, yes multiple reasoning steps layered over and over.
Most of these inner questioning and mind voice thingies provide simple answers, the majority of cases waiting for a yes/no answer for a self question before that triggers a reaction which triggers a prompt injection.
And that's where I found grammar, my salvation, just by doing root ::= "yes" | "no" .\*; and then having a custom kill switch on the first yes/no token, I was guaranteed a quick response which covered a lot of cases, some others were more complex, but still dynamically generated grammar just made compact answers saving tokens, and a lot of reasoning layers are heuristics and build upon themselves (allowing me to use cheap methods), predict potentials, etc... the actual processing is inference based; grammar alone gave me a 20x speedup (because the LLM kept not getting to point aka, one single yes token vs a bunch of random tokens with unclear answers despite instructions) which is legendary.
But this is not good enough, each inference reasoning layer is taking around 1 to 3 seconds on average, with a potential of 20-100 reasoning steps (despite heuristics optimization) that can add to up to 2 minutes of waiting where the character is just 🤔"hold up im thinking" what is worse it gets potentially compounded by other characters around, so if you have a large crowd they just go 🤔🤔🤔🤔🤔 as they start talking to each other and pumping their reasoning layers, and the better/worse the relationship among those characters the more they think because the more they have shared together.
I tried combining multiple questions into one but it just got confused.
Is it just a matter of hardware?... I don't find any other tricks. But I am so hardbent on making it work on a single 3090. :( | 2026-01-30T13:54:57 | https://www.reddit.com/r/LocalLLaMA/comments/1qr638p/potential_inference_speedup_tricks/ | boisheep | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qr638p | false | null | t3_1qr638p | /r/LocalLLaMA/comments/1qr638p/potential_inference_speedup_tricks/ | false | false | self | 2 | null |
LM Studio doesn't let continue generating a message anymore | 33 | I used LM studio for a long time and always liked it. Since my computer isn't nasa-level, I have to use quantized llms, and this means that often, to make them understand what I want, I needed to edit their answer with something along the lines of "Oh I see, you need me to..." and then click on the button that forced it to continue the generation based on the start I fed it.
After the latest update, I can't find the button to make the model continue an edited answer, for some reason they seem to have removed the most important feature of running models locally.
Did they move it or is it gone? Is there another similarly well curated and easy to use software to do that without complex setup? | 2026-01-30T13:45:54 | https://www.reddit.com/r/LocalLLaMA/comments/1qr5vdu/lm_studio_doesnt_let_continue_generating_a/ | PhyrexianSpaghetti | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qr5vdu | false | null | t3_1qr5vdu | /r/LocalLLaMA/comments/1qr5vdu/lm_studio_doesnt_let_continue_generating_a/ | false | false | self | 33 | null |
Why we went desktop and local-first for agents 6 months ago | 13 | We’ve been thinking a lot about first principles when building agent project, and one conclusion we keep coming back to is this:
The first thing you should optimize for is the agent’s capability ceiling.
From that perspective, a desktop-first agent architecture makes a lot of sense. A few reasons why:
**Context access**
If you want agents to be genuinely useful, they need real user context. On desktop, an agent can natively and seamlessly access local files, folders, running apps, logs, configs, and other artifacts that are either impossible or extremely awkward to reach from a purely web-based agent.
**Permissions equal intelligence**
Powerful agents need powerful permissions. Desktop agents can read and write the local file system, control native software like IDEs, terminals, browsers, or design tools, and make system-level calls or interact with hardware. This isn’t about being invasive, but about enabling workflows that simply don’t fit inside a web sandbox.
**Web parity without web limitations**
A desktop agent can still do everything a web agent can do, whether through an embedded Chromium environment or via browser-extension-style control. The reverse is not true: web agents can’t escape their sandbox.
**Cost structure**
An often overlooked point is that desktop agents run on user-owned compute. Browsers, terminals, and local tools all execute locally, which significantly reduces backend costs and makes high-frequency, long-running agents much more viable.
This line of thinking is what led us to build Eigent, the opensource alternative to cowork
Curious how others here think about:
* Desktop-first vs web-first agents
* Capability vs security trade-offs
* Whether “agent OS” is a real emerging category or just hype
Would love to hear thoughts from people building or running local agents! | 2026-01-30T13:45:45 | https://www.reddit.com/r/LocalLLaMA/comments/1qr5v9d/why_we_went_desktop_and_localfirst_for_agents_6/ | Farajizx | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qr5v9d | false | null | t3_1qr5v9d | /r/LocalLLaMA/comments/1qr5v9d/why_we_went_desktop_and_localfirst_for_agents_6/ | false | false | self | 13 | null |
My local LLM usecase | 10 | No matter how much you spent on hardware you simply cant get the same performance as the SOTA models at home. I am not only talking about the quality of the output but also PP and TG. I use LLM’s for vibe coding, as a oracle for asking technical questions in my field (system administrator/devops) and tagging bookmarks in Karakeep. For the “oracle” usecase I noticed the GPT-OSS 20b does a decent job and for tagging bookmarks Gemma 4b works also great. I run these models on a MBP M4 Pro with 24GB RAM. For vibecoding I use Claude Pro Subscription for 20 euro a month in combination with GLM 4.7 Code Subscription for when I reach my limits from the Claude subscription.
Now I wait for the M5 Mac Mini which should show great improvement with PP and settle with gemma 4b and GPT-OSS 20b. A current M4 Mac Mini with 256GB SSD and 32GB RAM costs around 1200 euro and as I work in the education sector I can also get some discount from Apple. I expect that the same configuration when the M5 is released will be more or less at the same price level (yes I know the situation with RAM prices etc but I can imagine Apple buys this in bulk and can keep the prices “low”). I think 256GB SSD is enough as the biggest size you can run as a model is around 30GB in theory and around 25GB in more practical uses.
So when the new Mac Mini is out I finally will get a dedicated LLM machine with M5, 32GB RAM and 256GB for around 1200 euros which fits nicely in my mini rack. What do do you guys think about this? | 2026-01-30T13:42:47 | https://www.reddit.com/r/LocalLLaMA/comments/1qr5sp3/my_local_llm_usecase/ | TheProtector0034 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qr5sp3 | false | null | t3_1qr5sp3 | /r/LocalLLaMA/comments/1qr5sp3/my_local_llm_usecase/ | false | false | self | 10 | null |
PaddleOCR-VL 1.5 | 30 | PaddleOCR-VL 1.5 seems to have been released yesterday but hasn't been mentioned in this sub yet. Looks like an excellent update! | 2026-01-30T13:29:38 | https://www.paddleocr.ai/latest/en/index.html | iLaurens | paddleocr.ai | 1970-01-01T00:00:00 | 0 | {} | 1qr5hij | false | null | t3_1qr5hij | /r/LocalLLaMA/comments/1qr5hij/paddleocrvl_15/ | false | false | default | 30 | null |
help with LLM selection for local setup | 1 | my setup is a 5060 gpu with 8gb vram and 32gb ram. I know it isnt great but i wanted to know which latest llm is best for my needs. i need it to be decent at coding and with undergrad level math . any llm that can run at decent tps is good enough as long as their output is accurate most of the times. | 2026-01-30T13:18:47 | https://www.reddit.com/r/LocalLLaMA/comments/1qr58cb/help_with_llm_selection_for_local_setup/ | cool_karma1 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qr58cb | false | null | t3_1qr58cb | /r/LocalLLaMA/comments/1qr58cb/help_with_llm_selection_for_local_setup/ | false | false | self | 1 | null |
UPDATE: sklearn-diagnose now has an Interactive Chatbot! | 0 | I'm excited to share a major update to sklearn-diagnose - the open-source Python library that acts as an "MRI scanner" for your ML models (https://www.reddit.com/r/LocalLLaMA/s/JfKhNJs8iM)
When I first released sklearn-diagnose, users could generate diagnostic reports to understand why their models were failing. But I kept thinking - what if you could talk to your diagnosis? What if you could ask follow-up questions and drill down into specific issues?
Now you can! 🚀
🆕 What's New: Interactive Diagnostic Chatbot
Instead of just receiving a static report, you can now launch a local chatbot web app to have back-and-forth conversations with an LLM about your model's diagnostic results:
💬 Conversational Diagnosis - Ask questions like "Why is my model overfitting?" or "How do I implement your first recommendation?"
🔍 Full Context Awareness - The chatbot has complete knowledge of your hypotheses, recommendations, and model signals
📝 Code Examples On-Demand - Request specific implementation guidance and get tailored code snippets
🧠 Conversation Memory - Build on previous questions within your session for deeper exploration
🖥️ React App for Frontend - Modern, responsive interface that runs locally in your browser
GitHub: https://github.com/leockl/sklearn-diagnose
Please give my GitHub repo a star if this was helpful ⭐ | 2026-01-30T13:18:23 | https://www.reddit.com/r/LocalLLaMA/comments/1qr5804/update_sklearndiagnose_now_has_an_interactive/ | lc19- | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qr5804 | false | null | t3_1qr5804 | /r/LocalLLaMA/comments/1qr5804/update_sklearndiagnose_now_has_an_interactive/ | false | false | self | 0 | {'enabled': False, 'images': [{'id': 'DSlCfYhkXxmGPsOJwQlsk1egsWtpemRKm8HUsp6UeP4', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/DSlCfYhkXxmGPsOJwQlsk1egsWtpemRKm8HUsp6UeP4.png?width=108&crop=smart&auto=webp&s=c2df4da62ed5a228a0456368ccb6c1953322fa6d', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/DSlCfYhkXxmGPsOJwQlsk1egsWtpemRKm8HUsp6UeP4.png?width=216&crop=smart&auto=webp&s=b2a36d3cd1cf969311c67c81f54de7344522bc0a', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/DSlCfYhkXxmGPsOJwQlsk1egsWtpemRKm8HUsp6UeP4.png?width=320&crop=smart&auto=webp&s=a35d54d24fe60f39a9df1c116dbfde781b0359ae', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/DSlCfYhkXxmGPsOJwQlsk1egsWtpemRKm8HUsp6UeP4.png?width=640&crop=smart&auto=webp&s=eef6c3d22d08d8d0b0a28e00e0ea7b8e58c48f1e', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/DSlCfYhkXxmGPsOJwQlsk1egsWtpemRKm8HUsp6UeP4.png?width=960&crop=smart&auto=webp&s=6b501948630328b2e5d1a619e7740dfe6e2f02fc', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/DSlCfYhkXxmGPsOJwQlsk1egsWtpemRKm8HUsp6UeP4.png?width=1080&crop=smart&auto=webp&s=a4223158acacf7c0e53f621f37db15347fd2eb2d', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/DSlCfYhkXxmGPsOJwQlsk1egsWtpemRKm8HUsp6UeP4.png?auto=webp&s=a2757739d1f55e2ac3a7f57314e8685f7ade51d0', 'width': 1200}, 'variants': {}}]} |
We should really try fine-tuning MoLE model from a pre-trained model | 5 | tl;dr new architecture MoLE could let us run larger models locally by offloading to SSD at great speeds, but companies likely won't pre-train models with it, so I think it warrants a discussion on converting pre-trained models.
For context: read the [paper](https://arxiv.org/abs/2503.15798) and this [recent post](https://www.reddit.com/r/LocalLLaMA/comments/1qo75sj/mixture_of_lookup_experts_are_god_tier_for_the/) here on the subject. I'll try to be brief. Also, I used no LLMs to write this.
We have this new architecture called Mixture of Lookup Experts, which could be great esp. for local LLMs, because:
1. It loads only a small number of parameters per token compared to MoE (MB's vs GB's of memory moved)
2. Thanks to 1. we can offload everything into disk, like an SSD, still at reasonable speeds
3. It also performs less computation per token overall.
There are caveats of course, namely
1. It's novel, so we don't know if this scales very well yet\[\^1\]
2. It may require a lot of storage capacity, even if disk\[\^2\]
3. Training MoLE models is very expensive\[\^3\]
Given these, esp. 3., it sounds unlikely we'll see companies pre-training large MoLE models for now. So instead, it got me wondering: **could we convert a pre-trained model into MoLE?**
Now, I can prove that it is possible to "convert" traditional Transformer models\[\^4\] to MoLE losslessly. By that I mean:
"If a FFN layer is given by f(x) = W\_down ⋅ σ(W\_up ⋅ x), we can define our converted MoLE to have W\_down and σ as the routing mechanism, and W\_up as the expert value vectors (using the same values for every token)"
It's a bit of a silly statement, since it's just relabeling components. Since all tokens have the same parameters, we are not taking advantage of the vocabulary sparsity of MoLE at all, so this uses a *ton* of experts per token. But it shows that a perfect conversion is possible, to some degree.
The question is, how far can we reduce the number of experts per token from there, at acceptable performance loss? And how... does one do that?
I don't know. I know enough to say confidently that we'd need fine-tuning to do this, since the routing mechanism is context-sensitive. If we want to take advantage of the per-token parameters, we need to have sample data that contains these tokens, I think.
I also suggest focusing on smaller models first, like Qwen3 30B A3B, or even small dense models, as they're easier to experiment with.
I also know it could be very hard to pull off, given how challenging it is to MoE-ify or BitNet-ify existing models.
Beyond that, my ideas are just ideas. I'm a CS student and I had classes on ML, and passion for the field, but that's about it. I do think this approach has big potential, and I hope this post brings some attention to it.
If you have any opinions or suggestions, or know other relevant research, feel free to share here! If you know better online spaces for this discussion to take place, let me know as well. Thank you.
# Footnotes
\[\^1\]: The main argument is that the experts are fixed parameters that only depend on the token id, while real MoEs are mini MLPs that compute based on the context. However, you could counter-argument this since the routing mechanism in MoLE still depends on context, and in fact, I prove an equivalence between MoLE and FFNs/MoE, for sufficiently many experts.
\[\^2\]: From the other post I linked, I saw someone estimate 50TB for Kimi K2.5 (1T model), or 12.5TB at FP4. For models \~230B, this is morel like 4TB. But even then, this assumes one MoLE "expert" is equivalent to an MoE expert, which is unlikely. We'd likely need to find ways to better compress it.
\[\^3\]: The main issue is MoLE activates every expert for each token, since the sparsity is on the vocabulary axis. And since during training, each expert is a separate small MLP, this gets prohibitively expensive at scale.
\[\^4\]: You can also convert SwiGLU models with this, though it is trickier. MoEs also require extra hierarchy so you could group the lookup experts to choose top-k, but the argument stands. | 2026-01-30T13:02:32 | https://www.reddit.com/r/LocalLLaMA/comments/1qr4uwr/we_should_really_try_finetuning_mole_model_from_a/ | z_latent | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qr4uwr | false | null | t3_1qr4uwr | /r/LocalLLaMA/comments/1qr4uwr/we_should_really_try_finetuning_mole_model_from_a/ | false | false | self | 5 | null |
CPU-only interference (ik_llama.cpp) | 4 | Hello!
I'd like to share my results of the CPU-only interference (ik\_llama.cpp)
Compilation settings:
AVX = 1 | AVX\_VNNI = 0 | AVX2 = 1 | AVX512 = 0 | AVX512\_VBMI = 0 | AVX512\_VNNI = 0 | AVX512\_BF16 = 0 | FMA = 1 | NEON = 0 | SVE = 0 | ARM\_FMA = 0 | F16C = 1 | FP16\_VA = 0 | WASM\_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL\_INT8 = 0
Results:
oss-120b
**\***:**\~/ik\_llama.cpp**$ OMP\_NUM\_THREADS=64 ./build/bin/llama-bench -m \~/Downloads/gpt-oss-120b-Q4\_K\_M-00001-of-00002.gguf -t 64 -b 4096 -ub 4096 -ctk q8\_0 -fa 1 -rtr 1 -mla 3 -amb 1024 -r 5
| model | size | params | backend | threads | n\_batch | n\_ubatch | type\_k | amb | rtr | test | t/s |
| ------------------------------ | ---------: | ---------: | ---------- | ------: | ------: | -------: | -----: | ----: | --: | ------------: | ---------------: |
============ Repacked 145 tensors
===================================== llama\_new\_context\_with\_model: f16
======================================= HAVE\_FANCY\_SIMD is NOT defined
| gpt-oss ?B Q4\_K - Medium | 58.45 GiB | 116.83 B | BLAS | 64 | 4096 | 4096 | q8\_0 | 1024 | 1 | pp512 | 275.00 ± 38.36 |
===================================== llama\_new\_context\_with\_model: f16
| gpt-oss ?B Q4\_K - Medium | 58.45 GiB | 116.83 B | BLAS | 64 | 4096 | 4096 | q8\_0 | 1024 | 1 | tg128 | 35.34 ± 0.04 |
**\***:**\~/ik\_llama.cpp**$ OMP\_NUM\_THREADS=64 ./build/bin/llama-bench -m \~/Downloads/gpt-oss-120b-Q4\_K\_M-00001-of-00002.gguf -t 64 -b 4096 -ub 4096 -ctk q8\_0 -fa 1 -rtr 1 -mla 3 -amb 1024 -p 16384 -n 1024
| model | size | params | backend | threads | n\_batch | n\_ubatch | type\_k | amb | rtr | test | t/s |
| ------------------------------ | ---------: | ---------: | ---------- | ------: | ------: | -------: | -----: | ----: | --: | ------------: | ---------------: |
============ Repacked 145 tensors
===================================== llama\_new\_context\_with\_model: f16
======================================= HAVE\_FANCY\_SIMD is NOT defined
| gpt-oss ?B Q4\_K - Medium | 58.45 GiB | 116.83 B | BLAS | 64 | 4096 | 4096 | q8\_0 | 1024 | 1 | pp16384 | 185.45 ± 1.82 |
===================================== llama\_new\_context\_with\_model: f16
double free or corruption (!prev)
Aborted (core dumped)
minimax m.2.1.
**serv@serv-MZ32-AR1-00**:**\~/ik\_llama.cpp**$ OMP\_NUM\_THREADS=64 ./build/bin/llama-bench -m \~/Downloads/unsloth\_MiniMax-M2.1-GGUF\_UD-Q3\_K\_XL\_MiniMax-M2.1-UD-Q3\_K\_XL-00001-of-00003.gguf -t 64 -b 4096 -ub 4096 -ctk q8\_0 -fa 1 -rtr 1 -mla 3 -amb 1024 -r 5
| model | size | params | backend | threads | n\_batch | n\_ubatch | type\_k | amb | rtr | test | t/s |
| ------------------------------ | ---------: | ---------: | ---------- | ------: | ------: | -------: | -----: | ----: | --: | ------------: | ---------------: |
x============ Repacked 435 tensors
===================================== llama\_new\_context\_with\_model: f16
======================================= HAVE\_FANCY\_SIMD is NOT defined
| minimax-m2 230B.A10B Q3\_K - Medium | 94.33 GiB | 228.69 B | BLAS | 64 | 4096 | 4096 | q8\_0 | 1024 | 1 | pp512 | 165.27 ± 29.80 |
===================================== llama\_new\_context\_with\_model: f16
| minimax-m2 230B.A10B Q3\_K - Medium | 94.33 GiB | 228.69 B | BLAS | 64 | 4096 | 4096 | q8\_0 | 1024 | 1 | tg128 | 20.58 ± 0.04 |
build: 686fd1eb (4155)
**\***:**\~/ik\_llama.cpp**$ OMP\_NUM\_THREADS=64 ./build/bin/llama-bench -m \~/Downloads/unsloth\_MiniMax-M2.1-GGUF\_UD-Q3\_K\_XL\_MiniMax-M2.1-UD-Q3\_K\_XL-00001-of-00003.gguf -t 64 -b 4096 -ub 4096 -ctk q8\_0 -fa 1 -rtr 1 -mla 3 -amb 1024 -p 16384 -n 1024
| model | size | params | backend | threads | n\_batch | n\_ubatch | type\_k | amb | rtr | test | t/s |
| ------------------------------ | ---------: | ---------: | ---------- | ------: | ------: | -------: | -----: | ----: | --: | ------------: | ---------------: |
============ Repacked 435 tensors
===================================== llama\_new\_context\_with\_model: f16
======================================= HAVE\_FANCY\_SIMD is NOT defined
| minimax-m2 230B.A10B Q3\_K - Medium | 94.33 GiB | 228.69 B | BLAS | 64 | 4096 | 4096 | q8\_0 | 1024 | 1 | pp16384 | 76.19 ± 0.70 |
===================================== llama\_new\_context\_with\_model: f16
| minimax-m2 230B.A10B Q3\_K - Medium | 94.33 GiB | 228.69 B | BLAS | 64 | 4096 | 4096 | q8\_0 | 1024 | 1 | tg1024 | 19.01 ± 0.16 |
**\*\~/ik\_llama.cpp**$ OMP\_NUM\_THREADS=128 ./build/bin/llama-bench -m \~/Downloads/unsloth\_MiniMax-M2.1-GGUF\_UD-Q3\_K\_XL\_MiniMax-M2.1-UD-Q3\_K\_XL-00001-of-00003.gguf -t 128 -b 4096 -ub 4096 -ctk q8\_0 -fa 1 -mla 3 -amb 1024 -p 8192 -n 1024
| model | size | params | backend | threads | n\_batch | n\_ubatch | type\_k | amb | test | t/s |
| ------------------------------ | ---------: | ---------: | ---------- | ------: | ------: | -------: | -----: | ----: | ------------: | ---------------: |
===================================== llama\_new\_context\_with\_model: f16
======================================= HAVE\_FANCY\_SIMD is NOT defined
| minimax-m2 230B.A10B Q3\_K - Medium | 94.33 GiB | 228.69 B | BLAS | 128 | 4096 | 4096 | q8\_0 | 1024 | pp8192 | 113.05 ± 0.80 |
===================================== llama\_new\_context\_with\_model: f16
| minimax-m2 230B.A10B Q3\_K - Medium | 94.33 GiB | 228.69 B | BLAS | 128 | 4096 | 4096 | q8\_0 | 1024 | tg1024 | 16.40 ± 0.02 |
build: 686fd1eb (4155)
Also I have 1 amd radeon mi50 32gb, but can't connect it to the motherboard yet due to the size limitations, I'm waiting for the delivery of long riser. Sadly amd cards doesn't work with ik\_llama, so I'll lose CPU optimizations.
I'd be happy to learn about other people experience, building and running optimization tricks!
| 2026-01-30T12:58:51 | https://www.reddit.com/r/LocalLLaMA/comments/1qr4ro8/cpuonly_interference_ik_llamacpp/ | ZealousidealBunch220 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qr4ro8 | false | null | t3_1qr4ro8 | /r/LocalLLaMA/comments/1qr4ro8/cpuonly_interference_ik_llamacpp/ | false | false | self | 4 | null |
Yann LeCun says the best open models are not coming from the West. Researchers across the field are using Chinese models. Openness drove AI progress. Close access, and the West risks slowing itself. | 1,277 | From Forbes on YouTube: Yann LeCun Gives Unfiltered Take On The Future Of AI In Davos: [https://www.youtube.com/watch?v=MWMe7yjPYpE](https://www.youtube.com/watch?v=MWMe7yjPYpE)
Video by vitrupo on 𝕏: [https://x.com/vitrupo/status/2017218170273313033](https://x.com/vitrupo/status/2017218170273313033) | 2026-01-30T12:55:38 | https://v.redd.it/n31pvrxchhgg1 | Nunki08 | v.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1qr4p4x | false | {'reddit_video': {'bitrate_kbps': 2400, 'dash_url': 'https://v.redd.it/n31pvrxchhgg1/DASHPlaylist.mpd?a=1772369754%2CNDI2MzU1NzcwYTZmOWI2MDNhMjBhMWQ4MmVjNzEwNDhkNDllNTY0YmRmMTQxNTZhMzg5MjE3YmRiM2ZiMGIzZQ%3D%3D&v=1&f=sd', 'duration': 99, 'fallback_url': 'https://v.redd.it/n31pvrxchhgg1/CMAF_720.mp4?source=fallback', 'has_audio': True, 'height': 720, 'hls_url': 'https://v.redd.it/n31pvrxchhgg1/HLSPlaylist.m3u8?a=1772369754%2CZDVmMTFkODZiNGVjNjA5YTY4NWNiZmU4ZTg5YWY4NDhmYTVhODhjMDY3ZTEzMmNiYTc0NTI1Y2EwMTViMjk2ZQ%3D%3D&v=1&f=sd', 'is_gif': False, 'scrubber_media_url': 'https://v.redd.it/n31pvrxchhgg1/CMAF_96.mp4', 'transcoding_status': 'completed', 'width': 960}} | t3_1qr4p4x | /r/LocalLLaMA/comments/1qr4p4x/yann_lecun_says_the_best_open_models_are_not/ | false | false | 1,277 | {'enabled': False, 'images': [{'id': 'MnNnNHZ6eGNoaGdnMcC0w-E97YmQ2Bn80LEN79By6gOnSLJ7DXbqces3JuUE', 'resolutions': [{'height': 81, 'url': 'https://external-preview.redd.it/MnNnNHZ6eGNoaGdnMcC0w-E97YmQ2Bn80LEN79By6gOnSLJ7DXbqces3JuUE.png?width=108&crop=smart&format=pjpg&auto=webp&s=91a009d7aec8f3d08b260831096f5645a08e8f19', 'width': 108}, {'height': 162, 'url': 'https://external-preview.redd.it/MnNnNHZ6eGNoaGdnMcC0w-E97YmQ2Bn80LEN79By6gOnSLJ7DXbqces3JuUE.png?width=216&crop=smart&format=pjpg&auto=webp&s=7ef42baf56d2251f0ce483f67bc3460cfbf1a9cf', 'width': 216}, {'height': 240, 'url': 'https://external-preview.redd.it/MnNnNHZ6eGNoaGdnMcC0w-E97YmQ2Bn80LEN79By6gOnSLJ7DXbqces3JuUE.png?width=320&crop=smart&format=pjpg&auto=webp&s=79622bc9da5856f61cb8a20d7723c433bae5b95b', 'width': 320}, {'height': 480, 'url': 'https://external-preview.redd.it/MnNnNHZ6eGNoaGdnMcC0w-E97YmQ2Bn80LEN79By6gOnSLJ7DXbqces3JuUE.png?width=640&crop=smart&format=pjpg&auto=webp&s=500463b2b17611115241298a5a1e01375f0508c9', 'width': 640}, {'height': 720, 'url': 'https://external-preview.redd.it/MnNnNHZ6eGNoaGdnMcC0w-E97YmQ2Bn80LEN79By6gOnSLJ7DXbqces3JuUE.png?width=960&crop=smart&format=pjpg&auto=webp&s=07bd21fb37d4fad0a8284fcac0a1632164fc519a', 'width': 960}], 'source': {'height': 720, 'url': 'https://external-preview.redd.it/MnNnNHZ6eGNoaGdnMcC0w-E97YmQ2Bn80LEN79By6gOnSLJ7DXbqces3JuUE.png?format=pjpg&auto=webp&s=ae120ff09ce6c31e4447a708fd31f81e06e4f0e2', 'width': 960}, 'variants': {}}]} | |
Is this budget hardware setup capable of running Minimax M2.1, GLM 4.7, Kimi K2.5? | 0 | Trying to assess how viable or not this build is for quantized large models and what the expected performance might be. Given the size of those models and my limited VRAM, I figured going octo channel could possibly help for these MoE models. But trying to figure out how to predict performance of these MoE models is tricky
40GB VRAM (8gb+16gb+16gb)
256gb ddr4 3200 ram (4x32gb + 4x32gb, hopefully capable of running at octochannel at cl22)
\-AMD RYZEN THREADRIPPER PRO 3945WX PROCESSOR
\-Gigabyte MC62-G40 Rev 1.0 Workstation Board WRX80
\-2060Super 8GB
\-5060Ti 16GB
\-5060Ti 16GB
\-teamgroup zeus t-force 64gb kit (2x32gb) ddr4 3200 cl20-22-22-46 1.2V
\-teamgroup zeus t-force 64gb kit (2x32gb) ddr4 3200 cl20-22-22-46 1.2V
\-rimlance ram 64gb kit (2x32gb) ddr4-3200 pc4-25600 2rx8 1.2V cl22 2519 non-ecc udimm
\-rimlance ram 64gb kit (2x32gb) ddr4-3200 pc4-25600 2rx8 1.2V cl22 2519 non-ecc udimm
\-Crucial P310 2TB SSD, PCIe Gen4 NVMe M.2 2280
\-Arctic Freezer 4U-M Rev. 2 CPU air cooler
\-SAMA P1200 1200W Platinum Power Supply – Fully Modular ATX 3.1 PSU
\-Antec C8, Fans not Included | 2026-01-30T12:44:29 | https://www.reddit.com/r/LocalLLaMA/comments/1qr4gei/is_this_budget_hardware_setup_capable_of_running/ | Careful_Breath_1108 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qr4gei | false | null | t3_1qr4gei | /r/LocalLLaMA/comments/1qr4gei/is_this_budget_hardware_setup_capable_of_running/ | false | false | self | 0 | null |
What is everyone's thoughts on this Clawdbot? | 0 | 2026-01-30T12:32:16 | https://www.youtube.com/watch?v=lOKCCtb8ZGg | jjbola971 | youtube.com | 1970-01-01T00:00:00 | 0 | {} | 1qr46v7 | false | {'oembed': {'author_name': 'Data Driven Josh', 'author_url': 'https://www.youtube.com/@DataDrivenJosh', 'height': 200, 'html': '<iframe width="356" height="200" src="https://www.youtube.com/embed/lOKCCtb8ZGg?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="Do Not Use OpenClaw Until You Watch This"></iframe>', 'provider_name': 'YouTube', 'provider_url': 'https://www.youtube.com/', 'thumbnail_height': 360, 'thumbnail_url': 'https://i.ytimg.com/vi/lOKCCtb8ZGg/hqdefault.jpg', 'thumbnail_width': 480, 'title': 'Do Not Use OpenClaw Until You Watch This', 'type': 'video', 'version': '1.0', 'width': 356}, 'type': 'youtube.com'} | t3_1qr46v7 | /r/LocalLLaMA/comments/1qr46v7/what_is_everyones_thoughts_on_this_clawdbot/ | false | false | default | 0 | null | |
[Math] Fine-tuning MoLE model from a pre-trained model? | 1 | For context: read the [paper](https://arxiv.org/abs/2503.15798) and a [recent post here](https://www.reddit.com/r/LocalLLaMA/comments/1qo75sj/mixture_of_lookup_experts_are_god_tier_for_the/) on the subject. Also, I apologize for the size of my post, but I guarantee I wrote it all by hand and thought about this carefully, so it might be interesting.
basically, we have a relatively new architecture called Mixture of Lookup Experts, which uses a different approach to sparsity than MoE, with the advantages that:
1. It only loads a small number of parameters per token (so it moves MB's of memory rather than GB's like MoE does)
2. Because of 1., you can afford to off-load all parameters to disk (like a 1TB SSD) at low performance cost
3. Also generally reduces computation needed per token during inference.
The caveats being that, well, it works very differently from anything we've seen before, so we don't know if it scales very well\[\^1\], it needs quite a bit of disk storage\[\^2\], and training one is very expensive.\[\^3\] Which is probably why no one has made one such model yet.
But if it worked, it could be big for running local LLMs locally.
My question is, **could we fine-tune a pre-trained model into a MoLE?** In other words, could we somehow train MoLE layers to replace the models' FFN layers? (even if for smaller models, such as Qwen3 30B A3B, or models that aren't MoE at all)
For a fact, I've figured out that you can represent any "traditional" FFN layer under the MoLE view with a deterministic process. Basically:
if normal FFN layers are given by f(x) = W\_down \* σ(W\_up \* x)
you can turn the W\_up matrix into the routing mechanism, use σ instead of softmax as the router's activation, and use the W\_down matrix as the values of the experts.
Now, under this formulation, every token gets the exact same expert vectors, so it's not *really* a useful application of the MoLE architecture. Also, if you were to do this to a (sparse) MoE, you'd need to add top-k selection. In general though, it's kinda... useless, but it at least shows that, if you have as many experts as hidden dimensions, it is (in a roundabout way) equivalent to a normal MLP.
The thing now is just, if we actually took advantage of every token having its own experts, how much lower could we go? And how exactly do we do that?
I don't have concrete answers to those. I know we'd definitely need fine-tuning, since the routing mechanism depends on context, and you only get context by training on some data. Other than that, all I have are ideas on training methods that have as much foundation as Tower of Pisa. I also remember reading about how it's surprisingly hard to MoE-ify or BitNet-ify pre-trained models, so I suspect we'd find similar challenges here.
Still, I'd love for this to become a reality, so anyone who may have some opinion or suggestion on this topic, please feel free to share! If you have suggestions of better online spaces for this kind of discussion, please let me know as well. Thank you.
\[\^1\]: Those "experts" are fixed vectors that depend only on the token ids, so they aren't as sensitive to context as a real MLP-based MoE would be. Though a counter-argument could be made that the routing mechanism, which scores how much each expert should contribute, is still context-sensitive, so the model can still learn to use that context ultimately.
\[\^2\]: Some people ran calculations on that post, where they estimate you'd need \~50TB for Kimi K2.5, which has 1T params, or 12.5TB with FP4. But for less massive models, they could maybe fit under a 4TB drive. I also feel like, with how redundant those tables would be, there should exist techniques to make them more compact, which we don't know yet.
\[\^3\]: Unlike MoE, all experts in each layer are activated, instead of only the top-k. Also, during training every expert is an MLP, that takes the token embeddings from the model's embedding table as input. If you have 100 experts per layer, you have 100 of those MLPs, and every one of those will be triggered for every token in the sequence. So you don't get the sparsity efficiency of MoE during training, *even though* inference is super cheap. | 2026-01-30T11:50:09 | https://www.reddit.com/r/LocalLLaMA/comments/1qr3bge/math_finetuning_mole_model_from_a_pretrained_model/ | z_latent | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qr3bge | false | null | t3_1qr3bge | /r/LocalLLaMA/comments/1qr3bge/math_finetuning_mole_model_from_a_pretrained_model/ | false | false | self | 1 | null |
Biology PI building multi-agent AI orchestrator - looking for feedback/collaborators | 1 | I'm a biology professor (France/Germany) who spent the last year building an AI development orchestration system:
* Multi-agent pipeline: planner → executor → critic → security scan
* Local LLM support (Ollama/Qwen) for privacy mode
* Multi-executor fallback (cheap models first, escalate if needed)
* Quality gates that iterate until code passes
Working prototype, still rough around the edges. Built it for my own needs.
Now trying to figure out if this is useful to others or just scratching my own itch. Looking for feedback from people who think about this stuff, and potentially collaborators.
Anyone here working on similar problems? What's missing in the current AI dev tooling landscape? | 2026-01-30T11:47:26 | https://www.reddit.com/r/LocalLLaMA/comments/1qr39kx/biology_pi_building_multiagent_ai_orchestrator/ | Own-Marzipan4488 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qr39kx | false | null | t3_1qr39kx | /r/LocalLLaMA/comments/1qr39kx/biology_pi_building_multiagent_ai_orchestrator/ | false | false | self | 1 | null |
Upgrade my rig with a €3000 budget – which setup would you pick? | 0 | Hi folks,
I want to upgrade my rig with a budget of €3000.
Currently, I have 2× RTX 3060 (12 GB VRAM each), 56 GB RAM, and a Ryzen 7 5700G.
My usage: mainly coding with local models. I usually run one model at a time, and I'm looking for a setup that allows a larger context window and better performance with higher quantization levels (q8 or fp16). I use local models to prepare my features (planning mode), then validate them with a SOTA model. The build mode uses either a local model or a small cloud model (like Haiku, Grok Code Fast, etc.).
What setup would you recommend?
1/ Refurbished Mac Studio M2 Max – 96 GB RAM (1 TB SSD)
2/ 2× RTX 4000 20 GB (360 GB/s) — I could keep one RTX 3060 for a total of 52 GB VRAM
3/ 1× RTX 4500 32 GB (896 GB/s) — I could keep both RTX 3060s for a total of 48 GB VRAM
The Mac probably offers the best capability for larger context sizes, but likely at the lowest raw speed.
Which one would you pick? | 2026-01-30T11:02:35 | https://www.reddit.com/r/LocalLLaMA/comments/1qr2gas/upgrade_my_rig_with_a_3000_budget_which_setup/ | yeswearecoding | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qr2gas | false | null | t3_1qr2gas | /r/LocalLLaMA/comments/1qr2gas/upgrade_my_rig_with_a_3000_budget_which_setup/ | false | false | self | 0 | null |
A question how do models like GPT have memory and constantly update it without increasing the context length so much? | 5 | Can we do that on LM Studio? | 2026-01-30T10:52:54 | https://www.reddit.com/r/LocalLLaMA/comments/1qr29yg/a_question_how_do_models_like_gpt_have_memory_and/ | Opening_Exit_1153 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qr29yg | false | null | t3_1qr29yg | /r/LocalLLaMA/comments/1qr29yg/a_question_how_do_models_like_gpt_have_memory_and/ | false | false | self | 5 | null |
How do you test LLM model changes before deployment? | 1 | Currently running a production LLM app and considering switching models (e.g., Claude → GPT-4o, or trying Gemini).
My current workflow:
\- Manually test 10-20 prompts
\- Deploy and monitor
\- Fix issues as they come up in production
I looked into AWS SageMaker shadow testing, but it seems overly complex for API-based LLM apps.
Questions for the community:
1. How do you validate model changes before deploying?
2. Is there a tool that replays production traffic against a new model?
3. Or is manual testing sufficient for most use cases?
Considering building a simple tool for this, but wanted to check if others have solved this already.
Thanks in advance. | 2026-01-30T10:48:56 | https://www.reddit.com/r/LocalLLaMA/comments/1qr27hi/how_do_you_test_llm_model_changes_before/ | Fluffy_Salary_5984 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qr27hi | false | null | t3_1qr27hi | /r/LocalLLaMA/comments/1qr27hi/how_do_you_test_llm_model_changes_before/ | false | false | self | 1 | null |
Rig for Local LLMs (RTX Pro 6000 vs Halo Strix vs DGX Spark) | 7 | Hello,
For some time I'm eyeing gear for setting up local LLMs. I've even got 2 3090(with plan to get 4 total) some time ago, but decided that setting up 4 of those would not be feasible for me at that time and I've returned them and I'm looking for different approach.
As for usage, there will probably be only one user at a time, maybe I'll expose it for my family, but I don't expect much concurrency there in general.
I plan to use it at least as some kind of personal assistant - emails and personal messages summary, accessing my private data, maybe private RAG (some clawdbot maybe?). That's the minimum requirement for me, since this may include some sensitive personal information, I can't use external LLMs for this. Other thing I'm interested in is coding - right now using Codex and I'm quite happy with it. I don't expect to get same results, but some coding capabilities would be welcome, but in this area I expect to loose some quality.
Now, I see three options (all the prices are after conversion from my local currency to USD):
\- RTX Pro 6000 ($10k)+ utilization of my current PC as server (I would need to get something as replacement for my PC) - best performance, possibility to upgrade in the future. Huge minus is cost of the card itself and having to get rest of the components, which with current ram prices is quite problematic.
\- Halo Strix (AI Max+ 395 with 128 GB of ram) ($3100) - way cheaper, but worse performance and also lack of possible upgrades (would running some occulink + RTX Pro 6000 be possible and beneficial as potential upgrade in te future? )
\- DGX Spark ($5300) - more expensive than AMD solution, still lack of upgrades. Seems to be way worse option than Halo Strix, but maybe I'm missing something?
I've found some estimations of 30-40 t/s for DGX Spark and Halo Strix and more than 120 t/s - are those realistic values?
Are there other, not obvious potential issues / benefits to consider?
| 2026-01-30T10:44:13 | https://www.reddit.com/r/LocalLLaMA/comments/1qr24ml/rig_for_local_llms_rtx_pro_6000_vs_halo_strix_vs/ | cysio528 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qr24ml | false | null | t3_1qr24ml | /r/LocalLLaMA/comments/1qr24ml/rig_for_local_llms_rtx_pro_6000_vs_halo_strix_vs/ | false | false | self | 7 | null |
I just gave a 4 hour lecture on building a mini-Clawdbot from Scratch | 0 | Github repository: [https://github.com/VizuaraAILabs/Slack-ClawdBot/](https://github.com/VizuaraAILabs/Slack-ClawdBot/)
Video: [https://youtu.be/sfi\_xebGsSw](https://youtu.be/sfi_xebGsSw)
It ran for 4 hours 30 minutes.
Here are topics I cover:
• Large Language Models foundations
• Retrieval‑Augmented Generation (RAG)
• Agents and MCP
• Context engineering that scales
• Memory and production grade memory architectures
I show how these pieces come together to build a powerful AI agent and AI assistant. | 2026-01-30T10:37:16 | https://www.reddit.com/r/LocalLLaMA/comments/1qr20at/i_just_gave_a_4_hour_lecture_on_building_a/ | OtherRaisin3426 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qr20at | false | null | t3_1qr20at | /r/LocalLLaMA/comments/1qr20at/i_just_gave_a_4_hour_lecture_on_building_a/ | false | false | self | 0 | {'enabled': False, 'images': [{'id': 'wuDMuTFL-iziXaJkgNGK2N8hsf0RhVeeUDTZVJzMCCM', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/wuDMuTFL-iziXaJkgNGK2N8hsf0RhVeeUDTZVJzMCCM.png?width=108&crop=smart&auto=webp&s=d15b439d3b5e6cf59bfce6df6996c95a519fd4e9', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/wuDMuTFL-iziXaJkgNGK2N8hsf0RhVeeUDTZVJzMCCM.png?width=216&crop=smart&auto=webp&s=2e264e2e4a323dc73bb1bad2e9791fa60e8e701e', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/wuDMuTFL-iziXaJkgNGK2N8hsf0RhVeeUDTZVJzMCCM.png?width=320&crop=smart&auto=webp&s=854544db8f68369cb2d7c8ceaee64d3ae441f90b', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/wuDMuTFL-iziXaJkgNGK2N8hsf0RhVeeUDTZVJzMCCM.png?width=640&crop=smart&auto=webp&s=de6c80715364a4b2d2ce969e9c9d73a3abb159e3', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/wuDMuTFL-iziXaJkgNGK2N8hsf0RhVeeUDTZVJzMCCM.png?width=960&crop=smart&auto=webp&s=bc541c1928d07d8817ee759fde86ab9f98e44b3d', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/wuDMuTFL-iziXaJkgNGK2N8hsf0RhVeeUDTZVJzMCCM.png?width=1080&crop=smart&auto=webp&s=7ce76222d4cffc8c98f30ead212660fc702b7ce2', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/wuDMuTFL-iziXaJkgNGK2N8hsf0RhVeeUDTZVJzMCCM.png?auto=webp&s=6ddedcd327270debbb9a1a11621440a7485f1da0', 'width': 1200}, 'variants': {}}]} |
Which Models do You Use for Writing Blog Post and Other Content? | 1 | [removed] | 2026-01-30T10:26:16 | https://www.reddit.com/r/LocalLLaMA/comments/1qr1tq2/which_models_do_you_use_for_writing_blog_post_and/ | leopold_researchly | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qr1tq2 | false | null | t3_1qr1tq2 | /r/LocalLLaMA/comments/1qr1tq2/which_models_do_you_use_for_writing_blog_post_and/ | false | false | self | 1 | null |
SenseTime have launched and open-sourced SenseNova-MARS (8B/32B)! | 1 | First open-source AgenticVLM with dynamic image reasoning + text/image search
Autonomously plans steps, calls various tools, solves complex tasks
SOTA across benchmarks including MMSearch, HR-MMSearch, FVQA and more — surpassing Gemini3Pro & GPT5.2
https://preview.redd.it/gdm9xsjvoggg1.jpg?width=900&format=pjpg&auto=webp&s=62b1690bae6ebe8b4e604d98538ec6e4b72af733 | 2026-01-30T10:18:39 | https://www.reddit.com/r/LocalLLaMA/comments/1qr1p1u/sensetime_have_launched_and_opensourced/ | Soggy_Mission3372 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qr1p1u | false | null | t3_1qr1p1u | /r/LocalLLaMA/comments/1qr1p1u/sensetime_have_launched_and_opensourced/ | false | false | 1 | null | |
Anyone using bitnet.cpp for production apps? | 0 | I have a backend service which does simple text sumarization and clasification (max 5 categories). At the moment I am using Digital Ocean agents (for price reasons) and hosted ollama instance with a 14B model running on a dedicated GPU.
Both solutions come with drawback.
The hosted ollama can process max 2 req/s on average depending on the input size. It is also not really scalable in terms of cost per value generated.
The DO agents are great and scalable. But they are also too expensive for the simple things I need.
For context: My pipeline processes a couple milion documents per day. Each about ~1500 tokens long.
I was reading and playing with bitnet.cpp. But before going too deep, I am curious if you guys can share your. experience and sucess/fail use cases in production systems.
| 2026-01-30T10:09:25 | https://www.reddit.com/r/LocalLLaMA/comments/1qr1j7b/anyone_using_bitnetcpp_for_production_apps/ | 4848928883 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qr1j7b | false | null | t3_1qr1j7b | /r/LocalLLaMA/comments/1qr1j7b/anyone_using_bitnetcpp_for_production_apps/ | false | false | self | 0 | null |
[Architecture] Applying "Charging Cable Topology" to System 2: Why We Should Stop Pruning Errors | 0 | I recently discussed the concept of "charging cable topology" (logical nodes providing structural rigidity). Since then, I've been researching how to translate this physical intuition into a specific System 2 architecture applicable to LLM agents.
With my background in intensive care units (ICUs) and industrial systems (HVAC), I believe there's a fundamental flaw in our current design of the Chain of Inference (CoT): we treat errors as noise that needs pruning.
The "Linear" Fallacy: In the ICU, we don't "eliminate" symptoms, we control them. In HVAC systems, if a beam blocks a pipe, we build a complex bypass. Standard CoT algorithms attempt to "straighten the cable"—backtracking and eliminating dead ends to find a clear linear path. But this creates a "fragile" chain of inference that breaks when the problem becomes too complex.
Proposal: Topological Memory (Implementation) My proposed module aims to consolidate errors, rather than using RAG (Retrieval of Facts) or standard CoT (Linear Path).
Here is the architectural logic I'm testing:
Persistence over Pruning: Do not reset the context when the agent encounters a logical contradiction.
Node Labeling: We record specific vector states as "high-resistance nodes."
Structural Pivot: In subsequent iterations, the model treats this node as an entity—a recoverable "knot," not a gap to be avoided.
Why do this? A system that can accurately remember its own error location constructs a three-dimensional map of the problem space. "Nodes" become scaffolding. Cables need to be coiled to maintain tension.
The Trap (Entropy) Of course, as many would point out: if we retain every error, the context window expands. A bunch of static nodes is nothing but garbage data.
This is where the second part of the architecture comes into play. To navigate smoothly in this "high-resistance topology" without getting stuck, we cannot use standard search methods. We need a dynamic force. I call it "gravitational navigation" (using the target mass as a teleological field).
I'm currently organizing my notes on this "gravitational" module. I plan to share the second part tomorrow, discussing how to balance this entropy.
(This is an attempt to combine physical topology with artificial intelligence reasoning. What are your thoughts on "error crystallization" and "pruning"?) | 2026-01-30T10:02:57 | https://www.reddit.com/r/LocalLLaMA/comments/1qr1f68/architecture_applying_charging_cable_topology_to/ | eric2675 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qr1f68 | false | null | t3_1qr1f68 | /r/LocalLLaMA/comments/1qr1f68/architecture_applying_charging_cable_topology_to/ | false | false | self | 0 | null |
Open-source LoongFlow: Bridging LLM-powered Reasoning Agents and Evolutionary Algorithms for Local AI Research | 2 | Hey r/LocalLLaMA community! I’ve been exploring tools to make LLM-based autonomous AI research more efficient, and wanted to share an open-source framework that’s been working well for me—LoongFlow. It’s designed to bridge reasoning agents (powered by LLMs) and evolutionary algorithms, and I think it could be helpful for anyone working on algorithm discovery, ML pipeline optimization, or LLM-based research.
If you’ve ever struggled with inefficient AI research or wasted computing power, you know the pain: Reasoning-based Agents (like AutoGPT, Voyager) are great at understanding tasks but lack large-scale exploration. Evolutionary algorithms (like MAP-Elites, OpenEvolve) excel at diverse search but rely on blind mutation without semantic guidance. LoongFlow merges these two strengths to create a more effective approach to directed cognitive evolution.
The core of LoongFlow is its Plan-Execute-Summarize (PES) cognitive paradigm—not just a simple combination, but a full closed loop. The Planner uses historical data and semantic reasoning to map the best evolution path, avoiding blind trial and error. The Executor runs parallel population-level optimization to explore diverse solutions. The Summarizer reviews results, learns from successes and failures, and feeds insights back to the Planner. This turns random trial and error into directed thinking, boosting both efficiency and quality.
Here’s a simple diagram to illustrate the PES cognitive paradigm (helps visualize the closed-loop logic):
https://preview.redd.it/mqllrhehkggg1.png?width=1024&format=png&auto=webp&s=672e114ad4c45cf5e808fa2182e3e714f7e1d567
I’ve seen some solid real-world results from it too. In algorithm discovery, it broke baselines in AlphaEvolve tests—scoring 0.9027 on Autocorrelation II (vs. 0.8962 for traditional frameworks) and advancing the Erdős problem. In ML, its built-in agent won 14 Kaggle/MLEBench gold medals (computer vision, NLP, tabular data) without any manual intervention. All of this is well-documented in its open-source repo, so you can verify the results yourself.
https://preview.redd.it/gjh3jlb7lggg1.png?width=627&format=png&auto=webp&s=70ac2ed41b0fbdaf940921e89bcc7c5c919c82af
As an open-source framework, LoongFlow offers a practical tool for LLM-based autonomous research. For years, AI research tools were limited to basic data processing and model training assistance. LoongFlow takes this further, enabling more independent AI-driven research—especially useful for those working with local LLMs and looking to avoid unnecessary computing power waste.
Best of all, it’s completely open-source and accessible to teams of any size, even for local deployment on consumer-grade hardware (no need for high-end GPUs). It comes with full code, pre-built Agents, and detailed documentation, supporting both open-source LLMs (like DeepSeek) and commercial ones (like Gemini). You don’t need huge R&D costs to access top-tier cognitive evolution capabilities—just clone the repo and get started with local testing.
GitHub repo: [https://github.com/baidu-baige/LoongFlow](https://github.com/baidu-baige/LoongFlow)
I wanted to share this with the community because I think it could help a lot of researchers and developers save time and avoid common pitfalls. Has anyone tried integrating evolutionary algorithms with local LLMs before? What do you think of the PES paradigm? Would you use this for your next research project? Drop your thoughts and questions below—I’m happy to discuss! | 2026-01-30T09:53:08 | https://www.reddit.com/r/LocalLLaMA/comments/1qr18xk/opensource_loongflow_bridging_llmpowered/ | EnvironmentTop7077 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qr18xk | false | null | t3_1qr18xk | /r/LocalLLaMA/comments/1qr18xk/opensource_loongflow_bridging_llmpowered/ | false | false | self | 2 | null |
Hey so, I made a kinda local multimodal token counter, I'd like feedback | 0 | Title says it all, just pushed a proper token counter since I needed one, it might be full of bugs and need fixes so I'm looking for feedback from you guys: it's [tokometer.dev](https://tokometer.dev)
Thank you, hope you guys find it useful:
It's basically giving estimates based on whatever argument I could find online, the only tokenizer that's 100% accurate is gemini via its own key, struggling to find ways to make claude and gpt accurate as well. Oh and, it can **split** text if tokens are too many, cus ykn... 32k tokens is kind of the performance limit.
I might have to add a simple text paster but for now it's about files. | 2026-01-30T09:50:41 | https://www.reddit.com/r/LocalLLaMA/comments/1qr17er/hey_so_i_made_a_kinda_local_multimodal_token/ | lgk01 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qr17er | false | null | t3_1qr17er | /r/LocalLLaMA/comments/1qr17er/hey_so_i_made_a_kinda_local_multimodal_token/ | false | false | self | 0 | null |
Just arrived from taiwan - Pegatron Nvidia GH200 server, 144GB HBM3e, 624GB RAM, 2U, so quiet can run under the desk. | 18 | 2026-01-30T09:29:20 | GPTrack__ai | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1qr0uxi | false | null | t3_1qr0uxi | /r/LocalLLaMA/comments/1qr0uxi/just_arrived_from_taiwan_pegatron_nvidia_gh200/ | false | false | 18 | {'enabled': True, 'images': [{'id': '-OtMMYRh6u2uXsGuTqzwic2E3cjZm-LxdzVqXG9NrNE', 'resolutions': [{'height': 81, 'url': 'https://preview.redd.it/befsk3kvgggg1.jpeg?width=108&crop=smart&auto=webp&s=cc3a9ef36502d42f2b18e6298c4cb26e1fb7f8c8', 'width': 108}, {'height': 162, 'url': 'https://preview.redd.it/befsk3kvgggg1.jpeg?width=216&crop=smart&auto=webp&s=66e00781ea214e86d0a2a8bc39e9c6a5c3d86bee', 'width': 216}, {'height': 240, 'url': 'https://preview.redd.it/befsk3kvgggg1.jpeg?width=320&crop=smart&auto=webp&s=d31924f292b97b71cd31b64023523a0228a2a223', 'width': 320}, {'height': 480, 'url': 'https://preview.redd.it/befsk3kvgggg1.jpeg?width=640&crop=smart&auto=webp&s=349fe347a3c8e98145a145033467743c173a0be5', 'width': 640}, {'height': 720, 'url': 'https://preview.redd.it/befsk3kvgggg1.jpeg?width=960&crop=smart&auto=webp&s=4cb826c00642339a618432eabc1097fd173bd67a', 'width': 960}, {'height': 810, 'url': 'https://preview.redd.it/befsk3kvgggg1.jpeg?width=1080&crop=smart&auto=webp&s=46b925f0216f39778cb26f599b9008ee069ba3fa', 'width': 1080}], 'source': {'height': 3456, 'url': 'https://preview.redd.it/befsk3kvgggg1.jpeg?auto=webp&s=3a85633beb78b809a04b321c7ebb5f9fc45e7f4f', 'width': 4608}, 'variants': {}}]} | |||
Beginner in RAG, Need help. | 17 | Hello, I have a 400-500 page unstructured PDF document with selectable text filled with Tables. I have been provided Nvidia L40S GPU for a week. I need help in parsing such PDf's to be able to run RAG on this. My task is to make RAG possible on such documents which span anywhere betwee 400 to 1000 pages. I work in pharma so i cant use any paid API's to parse this.
I have tried Camelot - didnt work well,
Tried Docling, works well but takes forever to parse 500 pages.
I thought of converting the PDF to Json, that didnt work so well either. I am new to all this, please help me with some idea on how to go forward. | 2026-01-30T09:28:13 | https://www.reddit.com/r/LocalLLaMA/comments/1qr0ubh/beginner_in_rag_need_help/ | whatshouldidotoknow | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qr0ubh | false | null | t3_1qr0ubh | /r/LocalLLaMA/comments/1qr0ubh/beginner_in_rag_need_help/ | false | false | self | 17 | null |
Is "Meta-Prompting" (asking AI to write your prompt) actually killing your reasoning results? A real-world A/B test. | 0 | Hi everyone,
I recently had a debate with a colleague about the best way to interact with LLMs (specifically Gemini 1.5 Pro).
* **His strategy (Meta-Prompting):** Always ask the AI to write a "perfect prompt" for your problem first, then use that prompt.
* **My strategy (Iterative/Chain-of-Thought):** Start with an open question, provide context where needed, and treat it like a conversation.
My colleague claims his method is superior because it structures the task perfectly. I argued that it might create a "tunnel vision" effect. So, we put it to the test with a real-world business case involving sales predictions for a hardware webshop.
**The Case:** We needed to predict the sales volume ratio between two products:
1. **Shims/Packing plates:** Used to level walls/ceilings.
2. **Construction Wedges:** Used to clamp frames/windows temporarily.
**The Results:**
**Method A: The "Super Prompt" (Colleague)** The AI generated a highly structured persona-based prompt ("Act as a Market Analyst...").
* **Result:** It predicted a conservative ratio of **65% (Shims) vs 35% (Wedges)**.
* **Reasoning:** It treated both as general "construction aids" and hedged its bet (Regression to the mean).
**Method B: The Open Conversation (Me)** I just asked: "Which one will be more popular?" and followed up with "What are the expected sales numbers?". I gave no strict constraints.
* **Result:** It predicted a massive difference of **8 to 1 (Ratio)**.
* **Reasoning:** Because the AI wasn't "boxed in" by a strict prompt, it freely associated and found a key variable: **Consumability**.
* *Shims* remain in the wall forever (100% consumable/recurring revenue).
* *Wedges* are often removed and reused by pros (low replacement rate).
**The Analysis (Verified by the LLM)** I fed both chat logs back to a different LLM for analysis. Its conclusion was fascinating: By using the "Super Prompt," we inadvertently constrained the model. We built a box and asked the AI to fill it. By using the "Open Conversation," the AI built the box itself. It was able to identify "hidden variables" (like the disposable nature of the product) that we didn't know to include in the prompt instructions.
**My Takeaway:** Meta-Prompting seems great for *Production* (e.g., "Write a blog post in format X"), but actually inferior for *Diagnosis & Analysis* because it limits the AI's ability to search for "unknown unknowns."
**The Question:** Does anyone else experience this? Do we over-engineer our prompts to the point where we make the model dumber? Or was this just a lucky shot? I’d love to hear your experiences with "Lazy Prompting" vs. "Super Prompting." | 2026-01-30T08:40:39 | https://www.reddit.com/r/LocalLLaMA/comments/1qr02n2/is_metaprompting_asking_ai_to_write_your_prompt/ | pinkstar97 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qr02n2 | false | null | t3_1qr02n2 | /r/LocalLLaMA/comments/1qr02n2/is_metaprompting_asking_ai_to_write_your_prompt/ | false | false | self | 0 | null |
OpenAI gives us adds, Google gives us this | 0 | 2026-01-30T08:10:37 | https://www.youtube.com/watch?v=YxkGdX4WIBE | idkwhattochoosz | youtube.com | 1970-01-01T00:00:00 | 0 | {} | 1qqzl35 | false | {'oembed': {'author_name': 'Google DeepMind', 'author_url': 'https://www.youtube.com/@googledeepmind', 'height': 200, 'html': '<iframe width="356" height="200" src="https://www.youtube.com/embed/YxkGdX4WIBE?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="Project Genie | Experimenting with infinite interactive worlds"></iframe>', 'provider_name': 'YouTube', 'provider_url': 'https://www.youtube.com/', 'thumbnail_height': 360, 'thumbnail_url': 'https://i.ytimg.com/vi/YxkGdX4WIBE/hqdefault.jpg', 'thumbnail_width': 480, 'title': 'Project Genie | Experimenting with infinite interactive worlds', 'type': 'video', 'version': '1.0', 'width': 356}, 'type': 'youtube.com'} | t3_1qqzl35 | /r/LocalLLaMA/comments/1qqzl35/openai_gives_us_adds_google_gives_us_this/ | false | false | default | 0 | null | |
Local AI setup | 4 | Hello, I currently have a Ryzen 5 2400G with 16 GB of RAM. Needless to say, it lags — it takes a long time to use even small models like Qwen-3 4B. If I install a cheap used graphics card like the Quadro P1000, would that speed up these small models and allow me to have decent responsiveness for interacting with them locally? | 2026-01-30T08:05:53 | https://www.reddit.com/r/LocalLLaMA/comments/1qqzi8r/local_ai_setup/ | Illustrious_Oven2611 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qqzi8r | false | null | t3_1qqzi8r | /r/LocalLLaMA/comments/1qqzi8r/local_ai_setup/ | false | false | self | 4 | null |
Local AI setup | 1 | Hi, I currently have a Ryzen 5 2400G with 16GB of RAM. To be honest, it’s lagging; it takes forever even for small models like Qwen 2.5 4B. If I get a cheap second-hand graphics card like a Quadro P1000, would that speed up these small models and give me decent response times for local interaction? | 2026-01-30T08:03:56 | https://www.reddit.com/r/LocalLLaMA/comments/1qqzh2d/local_ai_setup/ | Illustrious_Oven2611 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qqzh2d | false | null | t3_1qqzh2d | /r/LocalLLaMA/comments/1qqzh2d/local_ai_setup/ | false | false | self | 1 | null |
Local AI setup | 1 | [deleted] | 2026-01-30T08:02:40 | [deleted] | 1970-01-01T00:00:00 | 0 | {} | 1qqzgc3 | false | null | t3_1qqzgc3 | /r/LocalLLaMA/comments/1qqzgc3/local_ai_setup/ | false | false | default | 1 | null | ||
[Benchmark] Visualizing the "VRAM Wall": Qwen 2.5 7B (FP16) crashes to 0.7 TPS vs 50.9 TPS (Int4) on RTX 4070 Super (12GB). | 0 | Hi everyone,
I ran a benchmark to demonstrate exactly what happens when you exceed your GPU's VRAM limit. I attempted to run **Qwen 2.5 7B (FP16)** on my **RTX 4070 Super (12GB)**. Since the model size (\~14GB+) exceeds the 12GB VRAM, it was forced to swap to System RAM over the PCIe bus.
The result is a brutal **72x performance drop**.
**Hardware:**
* GPU: NVIDIA RTX 4070 Super (12GB)
* Backend: vLLM
**📊 Benchmark Results:**
|Model Format|Speed (TPS)|VRAM Usage|Notes|
|:-|:-|:-|:-|
|**FP16 (Unquantized)**|**0.7 TPS** 🐢|**15.36 GB** ⚠️|**OOM / System RAM Swap.** Unusable.|
|**AWQ (Int4)**|**50.9 TPS** 🚀|**9.86 GB** ✅|Fits comfortably in VRAM.|
**📝 Key Takeaways:**
1. **The VRAM Cliff:** Once you cross the 12GB limit, performance falls off a cliff. The difference between running in VRAM (50 TPS) and System RAM swap (0.7 TPS) is night and day.
2. **Quantization is Mandatory:** For 12GB cards, running 7B models in FP16 is not just "slower"—it's practically impossible. AWQ/Int4 is the only viable way to get decent speeds.
I visualized this VRAM behavior and speed comparison in a video here:
[**https://youtu.be/keJH5qHNLYk**](https://youtu.be/keJH5qHNLYk)
Just a friendly reminder to check your VRAM usage! | 2026-01-30T07:57:29 | https://www.reddit.com/r/LocalLLaMA/comments/1qqzczy/benchmark_visualizing_the_vram_wall_qwen_25_7b/ | Dry_Praline_4371 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qqzczy | false | null | t3_1qqzczy | /r/LocalLLaMA/comments/1qqzczy/benchmark_visualizing_the_vram_wall_qwen_25_7b/ | false | false | self | 0 | null |
[Benchmark] The Power of Quantization: Qwen 2.5 Coder 7B (Int4) is FASTER and LIGHTER than Qwen 2.5 3B (FP16) on RTX 4070 Super. | 0 | Hi everyone,
I ran a benchmark to visualize the impact of quantization on a 12GB VRAM card (RTX 4070 Super).
I compared a smaller unquantized model (**Qwen 2.5 3B @ FP16**) against a larger quantized model (**Qwen 2.5 Coder 7B @ AWQ Int4**).
The results were counter-intuitive but demonstrate why quantization is king for consumer GPUs.
**Hardware:**
* GPU: NVIDIA RTX 4070 Super (12GB)
* Backend: vLLM
**📊 Benchmark Results:**
|**Model**|**Format**|**Speed (TPS)**|**VRAM Usage**|
|:-|:-|:-|:-|
|**Qwen 2.5 3B**|FP16 (Unquantized)|35.9 TPS|**10.00 GB** ⚠️|
|**Qwen 2.5 Coder 7B**|**AWQ (Int4)**|**44.6 TPS** 🏆|**9.49 GB** ✅|
**📝 Analysis:**
1. **VRAM Efficiency:** Despite having more than double the parameters, the 7B (Int4) model uses **\~0.5GB LESS VRAM** than the 3B (FP16).
2. **Speed:** The 7B model is actually **\~24% faster** (44.6 vs 35.9 TPS).
3. **Conclusion:** For 12GB cards, there is almost no reason to run small models in FP16. You can run a much smarter 7B model faster and with less memory by using AWQ/GPTQ.
I made a video visualizing this comparison if you're interested in the real-time metrics.
[**https://youtu.be/mPmVJ0NcSU0**](https://youtu.be/mPmVJ0NcSU0)
Happy coding! | 2026-01-30T07:48:14 | https://www.reddit.com/r/LocalLLaMA/comments/1qqz7mi/benchmark_the_power_of_quantization_qwen_25_coder/ | Dry_Praline_4371 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qqz7mi | false | null | t3_1qqz7mi | /r/LocalLLaMA/comments/1qqz7mi/benchmark_the_power_of_quantization_qwen_25_coder/ | false | false | self | 0 | null |
Pro tip for the ones that wants to automate their lives using Molbot, Local Agents | 0 | AI can't fix a thing if your life is a mess.
Drink water, do exercise, say "good morning" to your neighbor (even if you hate it)
You'll realize it wasn't so hard to fix calendar, have better rest time, improve your social skills, or get some (human) help when you have problems.
Once you have that in order, run GLM 4.7 flash on your favourite agent tool and profit! | 2026-01-30T07:45:22 | https://www.reddit.com/r/LocalLLaMA/comments/1qqz5uu/pro_tip_for_the_ones_that_wants_to_automate_their/ | cristomc | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qqz5uu | false | null | t3_1qqz5uu | /r/LocalLLaMA/comments/1qqz5uu/pro_tip_for_the_ones_that_wants_to_automate_their/ | true | false | spoiler | 0 | null |
Tree style browser tabs are OP so I built tree-style terminal panes (OSS) | 2 | It's like an Obsidian-graph view but you can edit the markdown files and launch terminals directly inside of it. [github.com/voicetreelab/voicetree](http://github.com/voicetreelab/voicetree)
This helps a ton with brainstorming because I can represent my ideas exactly as they actually exist in my brain, as concepts as connections.
Then when I have coding agents help me execute these ideas, they are organised in the same space, so it's very easy to keep track of the state of various branches of work.
As I've learnt from spending the past year going heavy on agentic engineering, the bottleneck is ensuring the architecture of my codebase stays healthy. The mindmap aspect helps me plan code changes at a high level, spending most of my time thinking about how to best change my architecture to support. Once I am confident in the high level architectural changes, coding agents are usually good enough to handle the details, and when they do hit obstacles, all their progress is saved to the graph, so it's easy to change course and reference the previous planning artefacts. | 2026-01-30T07:41:51 | https://www.reddit.com/r/LocalLLaMA/comments/1qqz3ti/tree_style_browser_tabs_are_op_so_i_built/ | manummasson | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qqz3ti | false | null | t3_1qqz3ti | /r/LocalLLaMA/comments/1qqz3ti/tree_style_browser_tabs_are_op_so_i_built/ | false | false | self | 2 | {'enabled': False, 'images': [{'id': 'aPRSuHTPtFN9ZK2AQwOpbqgHm8zegKlAn9VnqpB846g', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/aPRSuHTPtFN9ZK2AQwOpbqgHm8zegKlAn9VnqpB846g.png?width=108&crop=smart&auto=webp&s=770a4a44ba1a1f98dcabb52db642c21acf245d0f', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/aPRSuHTPtFN9ZK2AQwOpbqgHm8zegKlAn9VnqpB846g.png?width=216&crop=smart&auto=webp&s=cabbab10d6f46d63d1de29bf5f4d4499a3aae2a3', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/aPRSuHTPtFN9ZK2AQwOpbqgHm8zegKlAn9VnqpB846g.png?width=320&crop=smart&auto=webp&s=966b524dc67772580c35466e6b6f4eaff327570f', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/aPRSuHTPtFN9ZK2AQwOpbqgHm8zegKlAn9VnqpB846g.png?width=640&crop=smart&auto=webp&s=92555688c8a7ab2e2ab3a679f01dccbb7faf0d4f', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/aPRSuHTPtFN9ZK2AQwOpbqgHm8zegKlAn9VnqpB846g.png?width=960&crop=smart&auto=webp&s=b1209e9e404f709542701274f8af2da45d097f29', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/aPRSuHTPtFN9ZK2AQwOpbqgHm8zegKlAn9VnqpB846g.png?width=1080&crop=smart&auto=webp&s=58e6b5982d7448c9dc148c93ab3e2415d6a63054', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/aPRSuHTPtFN9ZK2AQwOpbqgHm8zegKlAn9VnqpB846g.png?auto=webp&s=1ee96e9052d51f9739fc76a5773a2ca960553cb0', 'width': 1200}, 'variants': {}}]} |
I built a calculator that estimates LLM training cost & carbon footprint - Training a 1.3B model on 8x A100s: $4.1K, 8.5 days, 261 kg CO₂ (≈1,699 km driven) | 1 | [removed] | 2026-01-30T06:50:36 | https://www.reddit.com/r/LocalLLaMA/comments/1qqy8l4/i_built_a_calculator_that_estimates_llm_training/ | Rare_Pea7334 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qqy8l4 | false | null | t3_1qqy8l4 | /r/LocalLLaMA/comments/1qqy8l4/i_built_a_calculator_that_estimates_llm_training/ | false | false | 1 | null | |
Qwen3TTSVoiceClone | 0 | does any one know how to solve this issue? | 2026-01-30T06:39:12 | Chemical_Painter_431 | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1qqy1gj | false | null | t3_1qqy1gj | /r/LocalLLaMA/comments/1qqy1gj/qwen3ttsvoiceclone/ | false | false | default | 0 | {'enabled': True, 'images': [{'id': 'ax8yiz5gmfgg1', 'resolutions': [{'height': 53, 'url': 'https://preview.redd.it/ax8yiz5gmfgg1.png?width=108&crop=smart&auto=webp&s=cdac2b0a6c46672801c1fc83020ee68fe0372894', 'width': 108}, {'height': 107, 'url': 'https://preview.redd.it/ax8yiz5gmfgg1.png?width=216&crop=smart&auto=webp&s=dd577cb1257e439733f84684b309e7e545a2eac4', 'width': 216}, {'height': 159, 'url': 'https://preview.redd.it/ax8yiz5gmfgg1.png?width=320&crop=smart&auto=webp&s=1344976363ffedadb6bfeb80b0c38575e33807fb', 'width': 320}, {'height': 318, 'url': 'https://preview.redd.it/ax8yiz5gmfgg1.png?width=640&crop=smart&auto=webp&s=1053e6175493214e6386b5cc75097788990f9780', 'width': 640}, {'height': 477, 'url': 'https://preview.redd.it/ax8yiz5gmfgg1.png?width=960&crop=smart&auto=webp&s=f14e2654b47361b08c0fb158a3de34a7b17d4417', 'width': 960}, {'height': 536, 'url': 'https://preview.redd.it/ax8yiz5gmfgg1.png?width=1080&crop=smart&auto=webp&s=3705904c96cfc0ee3dc4917de64f497127fb27ac', 'width': 1080}], 'source': {'height': 916, 'url': 'https://preview.redd.it/ax8yiz5gmfgg1.png?auto=webp&s=3b4b684e1c1074a63143a027e36ad12d64b7745d', 'width': 1843}, 'variants': {}}]} | |
Tiny AI - new era of pocket sized AI computers | 0 | I just came by this one little clever box. Its still in pre-kickstarter phase but it looks very promising.
120-160 TOPS / 80gb ram / 1tb nvme all running on only 60watts
What do you think about it? For me, i just secured my place in line :)
https://tiiny.ai/ | 2026-01-30T06:05:45 | https://www.reddit.com/r/LocalLLaMA/comments/1qqxfh9/tiny_ai_new_era_of_pocket_sized_ai_computers/ | AdamLangePL | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qqxfh9 | false | null | t3_1qqxfh9 | /r/LocalLLaMA/comments/1qqxfh9/tiny_ai_new_era_of_pocket_sized_ai_computers/ | false | false | self | 0 | null |
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