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Perplexity >> Chrome (Comet) | 0 | Bro this Comet browser by Perplexity is like actually insane..... [https://pplx.ai/deepanshut23403](https://pplx.ai/deepanshut23403) | 2025-10-15T12:47:15 | https://www.reddit.com/r/LocalLLaMA/comments/1o7a1ds/perplexity_chrome_comet/ | Diligent_Debate6692 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o7a1ds | false | null | t3_1o7a1ds | /r/LocalLLaMA/comments/1o7a1ds/perplexity_chrome_comet/ | false | false | self | 0 | null |
guys glm 3 dollar plan is unlimited too in the api ?? bcz in the crush im getting this type of the detail on the crush cli , help me guys is this is costing or its irrelevant for me | 0 | 2025-10-15T12:27:06 | Select_Dream634 | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1o79l71 | false | null | t3_1o79l71 | /r/LocalLLaMA/comments/1o79l71/guys_glm_3_dollar_plan_is_unlimited_too_in_the/ | false | false | default | 0 | {'enabled': True, 'images': [{'id': 'mrm83er3r9vf1', 'resolutions': [{'height': 86, 'url': 'https://preview.redd.it/mrm83er3r9vf1.png?width=108&crop=smart&auto=webp&s=d55d15ae8c277bc7c94069006f5d36159202baee', 'width': 108}, {'height': 172, 'url': 'https://preview.redd.it/mrm83er3r9vf1.png?width=216&crop=smart&auto=webp&s=9e310060fae4d929137cf3a4b09fe39f748c31dc', 'width': 216}, {'height': 255, 'url': 'https://preview.redd.it/mrm83er3r9vf1.png?width=320&crop=smart&auto=webp&s=4a4df7db064a55966774f4a0bffc52efd7842689', 'width': 320}], 'source': {'height': 377, 'url': 'https://preview.redd.it/mrm83er3r9vf1.png?auto=webp&s=7aaf80ef87cabd971161a35a8887cf933f167f15', 'width': 472}, 'variants': {}}]} | ||
My TypeScript MCP server template `mcp-ts-template` just hit v2.3.7. Declarative tool definitions. Pluggable Storage. Edge-native (Cloudflare Workers). Optional OpenTelemetry. OAuth with Scope Enforcement, etc. | 3 | I've posted about my template once or twice before but it has evolved quite a bit into a really strong foundation for quickly building out custom MCP servers.
I've created quite a few MCP Servers (\~90k downloads) - you can see a list on my [GitHub Profile](https://github.com/cyanheads)
GitHub: [https://github.com/cyanheads/mcp-ts-template](https://github.com/cyanheads/mcp-ts-template)
Recent Additions:
* Declarative tool/resource system (define capabilities in single files, framework handles the rest)
* Works on Cloudflare Workers - very easy deployment!
* Swap storage backends (filesystem, Supabase, KV/R2) without changing logic
* Auth fully integrated (JWT/OAuth with scope enforcement)
* Full observability stack if you need it
* 93% test coverage
Ships with working examples (tools/resources/prompts) so you can clone and immediately understand the patterns.
Check it out & let me know if you have any questions or run into issues! | 2025-10-15T12:26:15 | cyanheads | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1o79kit | false | null | t3_1o79kit | /r/LocalLLaMA/comments/1o79kit/my_typescript_mcp_server_template_mcptstemplate/ | false | false | default | 3 | {'enabled': True, 'images': [{'id': 'epa30ypzq9vf1', 'resolutions': [{'height': 110, 'url': 'https://preview.redd.it/epa30ypzq9vf1.png?width=108&crop=smart&auto=webp&s=7bc2ca43bd3eff593345d3469cecccd8dfc35481', 'width': 108}, {'height': 221, 'url': 'https://preview.redd.it/epa30ypzq9vf1.png?width=216&crop=smart&auto=webp&s=e64e58b82e0047725820e0ec0be904ea3fa50d02', 'width': 216}, {'height': 327, 'url': 'https://preview.redd.it/epa30ypzq9vf1.png?width=320&crop=smart&auto=webp&s=01d14049b68d492327cf46ea4f8a0500c7dc8d27', 'width': 320}, {'height': 655, 'url': 'https://preview.redd.it/epa30ypzq9vf1.png?width=640&crop=smart&auto=webp&s=05f0ac8d9cdeb582c34bb5f83586f03f27580b9a', 'width': 640}], 'source': {'height': 941, 'url': 'https://preview.redd.it/epa30ypzq9vf1.png?auto=webp&s=342bafd8a143c228d071e93922ce9ea45f21ea84', 'width': 919}, 'variants': {}}]} | |
What is currently the fastest VLM? | 0 | I need to generate detailed descriptions of images in a predefined JSON schema and I need to do this as fast as possible for lots of images. The VLMs I’ve tried so far all take too long to produce the structured output.
What are today’s fastest VLMs or multimodal models that can reliably emit structured output (e.g. JSON, fixed schema)? | 2025-10-15T12:19:52 | https://www.reddit.com/r/LocalLLaMA/comments/1o79fp5/what_is_currently_the_fastest_vlm/ | jondoe9118 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o79fp5 | false | null | t3_1o79fp5 | /r/LocalLLaMA/comments/1o79fp5/what_is_currently_the_fastest_vlm/ | false | false | self | 0 | null |
Deep Dive into Nvidia's DGX Spark GB10 | 6 | 2025-10-15T12:10:07 | https://www.youtube.com/watch?v=Lqd2EuJwOuw | pscoutou | youtube.com | 1970-01-01T00:00:00 | 0 | {} | 1o798cj | false | {'oembed': {'author_name': 'Level1Techs', 'author_url': 'https://www.youtube.com/@Level1Techs', 'height': 200, 'html': '<iframe width="356" height="200" src="https://www.youtube.com/embed/Lqd2EuJwOuw?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="Deep Dive into Nvidia's DGX Spark GB10"></iframe>', 'provider_name': 'YouTube', 'provider_url': 'https://www.youtube.com/', 'thumbnail_height': 360, 'thumbnail_url': 'https://i.ytimg.com/vi/Lqd2EuJwOuw/hqdefault.jpg', 'thumbnail_width': 480, 'title': "Deep Dive into Nvidia's DGX Spark GB10", 'type': 'video', 'version': '1.0', 'width': 356}, 'type': 'youtube.com'} | t3_1o798cj | /r/LocalLLaMA/comments/1o798cj/deep_dive_into_nvidias_dgx_spark_gb10/ | false | false | 6 | {'enabled': False, 'images': [{'id': '7RK2-ldRyeO1G7cH7WvYJ5T5vh_Eu5lzsFNonawEwKQ', 'resolutions': [{'height': 81, 'url': 'https://external-preview.redd.it/7RK2-ldRyeO1G7cH7WvYJ5T5vh_Eu5lzsFNonawEwKQ.jpeg?width=108&crop=smart&auto=webp&s=4d026f27e33bfa63f7d03faa562cc7fe208e45d3', 'width': 108}, {'height': 162, 'url': 'https://external-preview.redd.it/7RK2-ldRyeO1G7cH7WvYJ5T5vh_Eu5lzsFNonawEwKQ.jpeg?width=216&crop=smart&auto=webp&s=162e027ee2ada69089716b9d3823f8bafdb4795b', 'width': 216}, {'height': 240, 'url': 'https://external-preview.redd.it/7RK2-ldRyeO1G7cH7WvYJ5T5vh_Eu5lzsFNonawEwKQ.jpeg?width=320&crop=smart&auto=webp&s=a2bc255a8fb6796dcc94e6bb0546062a3188356c', 'width': 320}], 'source': {'height': 360, 'url': 'https://external-preview.redd.it/7RK2-ldRyeO1G7cH7WvYJ5T5vh_Eu5lzsFNonawEwKQ.jpeg?auto=webp&s=9582514b01a44058a5013d306d02dfdf4dd1de58', 'width': 480}, 'variants': {}}]} | ||
Best Architecture for Multi-Role RAG System with Permission-Based Table Filtering and Data Extraction? | 3 | Here’s a clean, well-structured rewrite of your post without changing the context or technical details:
# Role-Aware RAG Retrieval — Architecture Advice Needed
Hey everyone! I’m working on a **voice assistant** that uses **RAG + semantic search (FAISS embeddings)** to query a large ERP database. I’ve run into an interesting architectural challenge and would love to hear your thoughts on it.
# 🎯 The Problem
The system supports multiple user roles — such as Regional Manager, District Manager, and Store Manager — each with different permissions. Depending on the user’s role, the **same query** should resolve against **different tables** and data scopes.
# Example:
* **Regional Manager** asks: *“What stores am I managing?”* → Should query: `regional_managers` → `districts` → `stores`
* **Store Manager** asks: *“What stores am I managing?”* → Should query: `store_managers` → `stores`
# 🧱 The Challenge
I need a way to make **RAG retrieval “role and permission-aware”** so that:
* Semantic search remains accurate and efficient.
* Queries are dynamically routed to the correct tables and scopes based on role and permissions.
* Future roles (e.g., Category Manager, Department Manager, etc.) with **custom permission sets** can be added without major architectural changes.
* Users can create roles dynamically by selecting store IDs, locations, districts, etc.
# 🏗️ Current Architecture
User Query
↓
fetch_erp_data(query)
↓
Semantic Search (FAISS embeddings)
↓
Get top 5 tables
↓
Generate SQL with GPT-4
↓
Execute & return results
# ❓ Open Question
What’s the best architectural pattern to make RAG retrieval **aware of user roles and permissions** — while keeping semantic search performant and flexible for future role expansions?
Any ideas, experiences, or design tips would be super helpful. Thanks in advance!
Disclaimer: Writtern by ChatGPT | 2025-10-15T12:08:47 | https://www.reddit.com/r/LocalLLaMA/comments/1o797cv/best_architecture_for_multirole_rag_system_with/ | Ai_Peep | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o797cv | false | null | t3_1o797cv | /r/LocalLLaMA/comments/1o797cv/best_architecture_for_multirole_rag_system_with/ | false | false | self | 3 | null |
Newbie here | 1 | Im a writer and i use Ai for brainstorming and also for grammar and ways to write better scenes in english since it aint my first language.
But since the new update gpt cant write or talk to anything regarding nsfw.
So i ask. I beg. I plead.
Help , any Ai and ideeas of how to use it. I only got a laptop with 8gb vram so idk if i can do much.
LM studio doesnt seem to work so i must ask again for your help for models , Ai , or anything.
Ty. Any help is apreciated.
(Im 117 pages deep into this book pls help) | 2025-10-15T12:05:01 | https://www.reddit.com/r/LocalLLaMA/comments/1o794ij/newbie_here/ | YellowR0 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o794ij | false | null | t3_1o794ij | /r/LocalLLaMA/comments/1o794ij/newbie_here/ | false | false | self | 1 | null |
New models Qwen3-VL-4b/8b: hands-on notes | 51 | I’ve got a pile of scanned PDFs, whiteboard photos, and phone receipts. The 4B Instruct fits well. For “read text fast and accurately,” the ramp-up is basically zero; most errors are formatting or extreme noise. Once it can read, I hand off to a text model for summarizing, comparison, and cleanup. This split beats forcing VQA reasoning on a small model.
For OCR + desktop/mobile GUI automation (“recognize → click → run flow”), the 8B Thinking is smooth. As a visual agent, it can spot UI elements and close the loop on tasks. The “visual coding enhancement” can turn screenshots into Draw.io/HTML/CSS/JS skeletons, which saves me scaffolding time.
Long videos: I search meeting recordings by keywords and the returned timestamps are reasonably accurate. The official notes mention structural upgrades for long-horizon/multi-scale (Interleaved‑MRoPE, DeepStack, Text–Timestamp Alignment). Net effect for me: retrieval feels more direct.
If I must nitpick: on complex logic or multi-step visual reasoning, the smaller models sometimes produce “looks right” answers. I don’t fight it, let them handle recognition; route reasoning to a bigger model. That’s more stable in production. I also care about spatial understanding, especially for UI/flowchart localization. From others’ tests, 2D/3D grounding looks solid this gen, finding buttons, arrows, and relative positions is reliable. For long/tall images, the 256K context (extendable to 1M) is friendly for multi-panel reading; cross-page references actually connect.
References: [https://huggingface.co/collections/Qwen/qwen3-vl-68d2a7c1b8a8afce4ebd2dbe](https://huggingface.co/collections/Qwen/qwen3-vl-68d2a7c1b8a8afce4ebd2dbe) | 2025-10-15T11:59:11 | https://www.reddit.com/r/LocalLLaMA/comments/1o78zuc/new_models_qwen3vl4b8b_handson_notes/ | chenqian615 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o78zuc | false | null | t3_1o78zuc | /r/LocalLLaMA/comments/1o78zuc/new_models_qwen3vl4b8b_handson_notes/ | false | false | self | 51 | {'enabled': False, 'images': [{'id': '_7BYCEiuSe8H_fldVM7chLfCb5j0ciz_pk_F5HpmBuY', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/_7BYCEiuSe8H_fldVM7chLfCb5j0ciz_pk_F5HpmBuY.png?width=108&crop=smart&auto=webp&s=7b214351fe21c158d99ec16f482edd00309859ed', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/_7BYCEiuSe8H_fldVM7chLfCb5j0ciz_pk_F5HpmBuY.png?width=216&crop=smart&auto=webp&s=c3b0c8771dd73cc356414d84d300ef2199012713', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/_7BYCEiuSe8H_fldVM7chLfCb5j0ciz_pk_F5HpmBuY.png?width=320&crop=smart&auto=webp&s=e86477bb3288eea01f24f26169bd4a4390ea0d9a', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/_7BYCEiuSe8H_fldVM7chLfCb5j0ciz_pk_F5HpmBuY.png?width=640&crop=smart&auto=webp&s=de9f337f24ae40e158287e6812e9408e53add8ae', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/_7BYCEiuSe8H_fldVM7chLfCb5j0ciz_pk_F5HpmBuY.png?width=960&crop=smart&auto=webp&s=877c69f46333897d84cc523374a04441effdfadf', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/_7BYCEiuSe8H_fldVM7chLfCb5j0ciz_pk_F5HpmBuY.png?width=1080&crop=smart&auto=webp&s=f7a7fe628fa35576f41a59a6bd5ca44f98e1581b', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/_7BYCEiuSe8H_fldVM7chLfCb5j0ciz_pk_F5HpmBuY.png?auto=webp&s=376c31bdcc896c240f287beabce9f8793d17c43e', 'width': 1200}, 'variants': {}}]} |
2x AMD GPUs: Is Llama.cpp still a good option? | 16 | For years I've been happy with 1x 7900xtx + llama.cpp-vulkan. But then, I got a second 7900xtx to join the big(ger) boys club, and now llama.cpp doen't seem to be a good option anymore:
* According to [llama.cpp feature matrix](https://github.com/ggml-org/llama.cpp/wiki/Feature-matrix), tensor parallel (**row split**) should be supported for ROCm (albeit poorly), but believe it or not, it has been *significantly* *slower* than **layer** split from my experience.
* ROCm offload-to-cpu behavior is different than Vulkan's. With Vulkan backend, you can stick -ngl 99 and it will shove as much layers into VRAM then the rest in RAM, automatically. With ROCm, -ngl N has to be carefully calculated or it will OOM.
* Models that fits comfortably in 48GB VRAM under vulkan, will fail to load with ROCm, it's as though the later consumes more VRAM.
So, with ROCm tensor parallel out of the window and Vulkan continues to be the better backend over all, I can hardly justify using llama.cpp anymore. I think it's time to investigate vLLM after getting over the horrific experience I had with vllm-rocm 1+ year ago.
But I wonder, what inference engines are the the multi-amd-gpu owners use? Am I doing something wrong with llama.cpp-hip. | 2025-10-15T11:52:57 | https://www.reddit.com/r/LocalLLaMA/comments/1o78vh7/2x_amd_gpus_is_llamacpp_still_a_good_option/ | ParaboloidalCrest | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o78vh7 | false | null | t3_1o78vh7 | /r/LocalLLaMA/comments/1o78vh7/2x_amd_gpus_is_llamacpp_still_a_good_option/ | false | false | self | 16 | {'enabled': False, 'images': [{'id': 'DJPqvteONpGwVVw6LzaG6b8vlDa2rv2hETCaqe0z57s', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/DJPqvteONpGwVVw6LzaG6b8vlDa2rv2hETCaqe0z57s.png?width=108&crop=smart&auto=webp&s=72aa5dcc1cd8dbddd3f1a103959106b666940069', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/DJPqvteONpGwVVw6LzaG6b8vlDa2rv2hETCaqe0z57s.png?width=216&crop=smart&auto=webp&s=a4159f87f341337a34069632ee0d5b75fa4e7042', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/DJPqvteONpGwVVw6LzaG6b8vlDa2rv2hETCaqe0z57s.png?width=320&crop=smart&auto=webp&s=b105a2c86f91fee19ce34c791a1b984348b68452', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/DJPqvteONpGwVVw6LzaG6b8vlDa2rv2hETCaqe0z57s.png?width=640&crop=smart&auto=webp&s=ae5173c455a88bb40bed1198799c0db65ff470d0', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/DJPqvteONpGwVVw6LzaG6b8vlDa2rv2hETCaqe0z57s.png?width=960&crop=smart&auto=webp&s=d014791efbd4c8d05fd305a8b7842b029f22d83e', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/DJPqvteONpGwVVw6LzaG6b8vlDa2rv2hETCaqe0z57s.png?width=1080&crop=smart&auto=webp&s=9addd19259612948921416b6f5bf04bd5191f933', 'width': 1080}], 'source': {'height': 640, 'url': 'https://external-preview.redd.it/DJPqvteONpGwVVw6LzaG6b8vlDa2rv2hETCaqe0z57s.png?auto=webp&s=db9ea157807723165a59f5f8694d9a5016d60d0f', 'width': 1280}, 'variants': {}}]} |
Which UI + server setup has proper implementation for continuing an edited assistant message? | 2 | I know I can do this easily with llama.cpp server and mikupad or oobabooga, but these seem pretty outdated these days. Still works, but not best supported.
I have been using the llama.cpp server UI most frequently, and it works well most of the time, but it does not support continue generating edited assistant message, only simple editing and regeneration.
LibreChat does seem to have an "edit and resubmit" option, but it returns very odd results, as if a separate assistant response were attached to the end of the message that was edited, not forming organic continuation at all.
Is this due to the built-in template of chat models preventing generating partial messages not adhering to a strict turn order? Should I just fall back to a simple completion-only UI like mikupad and forget about more advanced UI options? | 2025-10-15T11:38:00 | https://www.reddit.com/r/LocalLLaMA/comments/1o78l0u/which_ui_server_setup_has_proper_implementation/ | aeroumbria | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o78l0u | false | null | t3_1o78l0u | /r/LocalLLaMA/comments/1o78l0u/which_ui_server_setup_has_proper_implementation/ | false | false | self | 2 | null |
eGpu with two slots | 6 |
Hey guys right now I am running a laptop with rtx4090 with 16gb vram.
Unfortunately I can not run middle size models efficiently.
I was wondering if there is any eGPU that supports two rtx4090 or two rtx 5090.
I checked the new razer eGPU but unfortunately it only supports one gpu.
I plan on using it with thunderbolt 4.
Any recommendations?
| 2025-10-15T11:33:32 | https://www.reddit.com/r/LocalLLaMA/comments/1o78i0i/egpu_with_two_slots/ | Medium_Question8837 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o78i0i | false | null | t3_1o78i0i | /r/LocalLLaMA/comments/1o78i0i/egpu_with_two_slots/ | false | false | self | 6 | null |
Pretty new here. Been occasionally attempting to set up my own local LLM. Trying to find a reasoning model, not abliterated, that can do erotica, and has decent social nuance.. but so far it seems like they don't exist..? | 0 |
Not sure what front-end to use or where to start with setting up a form of memory.
Any advice or direction would be very helpful.
(I have a 4090, not sure if that's powerful enough for long contexts + memory + decent LLM (=15b-30b?) + long system prompt?) | 2025-10-15T11:16:17 | https://www.reddit.com/r/LocalLLaMA/comments/1o786f8/pretty_new_here_been_occasionally_attempting_to/ | WoodenTableBeach | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o786f8 | false | null | t3_1o786f8 | /r/LocalLLaMA/comments/1o786f8/pretty_new_here_been_occasionally_attempting_to/ | false | false | self | 0 | null |
Engineering convergence > bloated chain-of-thought? | 2 | A colleague dropped me a link to Ling-1T. First reaction: it markets itself as “non-thinking,” yet pushes hard on complex reasoning, a fun contrast. It claims 1T total params with \~50B active per token (MoE vibes), a reasoning-heavy pretrain mix, 128K context, plus Evo-CoT and LPO. Reads like “do the thinking upfront,” so reasoning paths are steadier and less fussy.
Two things I care about: (1) they say it can spar with closed APIs on reasoning-centric benchmarks, that matters for coding/problem-set style tasks; (2) the “non-thinking” framing feels like an engineered evolution of CoT: less rambling, more alignment. For folks who don’t want to handcraft prompt chains, this could be low-friction.
Quick hands-on: I expect it to pop first in code, math, and agent engineering. If you need heavy content safety or strict compliance, there’s likely a policy-tuning period ahead. Anyone got deeper usage notes or head-to-heads with other models?
* Hugging Face: [https://huggingface.co/inclusionAI/Ling-1T](https://huggingface.co/inclusionAI/Ling-1T)
* X : [https://x.com/AntLingAGI/status/1975942293330018426](https://x.com/AntLingAGI/status/1975942293330018426) | 2025-10-15T10:36:53 | https://www.reddit.com/r/LocalLLaMA/comments/1o77gwq/engineering_convergence_bloated_chainofthought/ | northwind2333 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o77gwq | false | null | t3_1o77gwq | /r/LocalLLaMA/comments/1o77gwq/engineering_convergence_bloated_chainofthought/ | false | false | self | 2 | {'enabled': False, 'images': [{'id': 'GF0ej-9rt3AXeKHvcKd5G-UgA8tEbZGSIvNEsQkOwA0', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/GF0ej-9rt3AXeKHvcKd5G-UgA8tEbZGSIvNEsQkOwA0.png?width=108&crop=smart&auto=webp&s=9298025b9f0e22dd433987dc8582bd4b418785f8', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/GF0ej-9rt3AXeKHvcKd5G-UgA8tEbZGSIvNEsQkOwA0.png?width=216&crop=smart&auto=webp&s=f2920cc91c8df7cc940c0738631412c91f032331', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/GF0ej-9rt3AXeKHvcKd5G-UgA8tEbZGSIvNEsQkOwA0.png?width=320&crop=smart&auto=webp&s=4e77554a0d2451d07bc2a029f6cdea35a8f8e9e4', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/GF0ej-9rt3AXeKHvcKd5G-UgA8tEbZGSIvNEsQkOwA0.png?width=640&crop=smart&auto=webp&s=acd86786f2d1ca8025b03b2b577396fa4bc316d8', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/GF0ej-9rt3AXeKHvcKd5G-UgA8tEbZGSIvNEsQkOwA0.png?width=960&crop=smart&auto=webp&s=b7af8dd66e9bae94a6380270da16e2da8976d614', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/GF0ej-9rt3AXeKHvcKd5G-UgA8tEbZGSIvNEsQkOwA0.png?width=1080&crop=smart&auto=webp&s=896b62c3d49f9c92fd97e208d944158df9fed442', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/GF0ej-9rt3AXeKHvcKd5G-UgA8tEbZGSIvNEsQkOwA0.png?auto=webp&s=3ab5924049080110c7117179489d24c473deab4c', 'width': 1200}, 'variants': {}}]} |
Is anyone else not getting any reasonable answers out of Qwen3-VL-4b MLX? | 9 | Using LM studio and the 4 bit MLX quant, Qwen3-VL-4b barely works at all. I gave it 3 test images of mine and asked it to describe them. Here are the results:
- An image with multiple graphs --> it did not see one of the graphs, mislabeled another, and gave a completely wrong description of what each of the graphs look like. At least it got the axis labels correctly, but everything else was almost random.
- A diagram with lots of arrows showing different heat transfer mechanisms --> It got all of the colors correctly, but then completely misread an information bubble (instead of "Ignoring: radiation inside" it read "igniter: Radiation/Conduction/Evaporation") and argued for this being a typo in the original image
- A scanned image of a brochure, asking for the highest-priced item on it --> it hallucinated prices, tables, and items before going into an infinite loop telling me the price of one (imaginary) item
Is anyone else surprised by how unusable this is? I am using the default parameters. | 2025-10-15T10:33:11 | https://www.reddit.com/r/LocalLLaMA/comments/1o77eox/is_anyone_else_not_getting_any_reasonable_answers/ | Anuin | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o77eox | false | null | t3_1o77eox | /r/LocalLLaMA/comments/1o77eox/is_anyone_else_not_getting_any_reasonable_answers/ | false | false | self | 9 | null |
A guide to the best agentic tools and the best way to use them on the cheap, locally or free | 39 | Did you expect an AI generated post? Complete with annoying emojis and GPTisms? I don't blame you. These AI generated posts are getting out of hand, and hurt to read. Vibe-coders seem to be some of the worst offenders of this. Am I a vibe coder too? Don't know. I don't really rely on AI coding much, but thought it was pretty neat, so I spent some weeks checking out various tools and models to get a feel for them. How I use them might be very different from others, so going to give that warning in advance. I prefer to write my code, then see if I can use the agent to either improve it some way (help with refactoring, making some my monolithic scripts more modular, writing tests, this kind of stuff), and sometimes trying to add features to my existing tools. I have tried one shotting a few tools from scratch with AI, but it wasn't for me, especially the agents that like to overengineer things and get carried away with it. I like knowing what my code is doing. If you are just getting into coding, I don't suggest relying on these tools heavily. I've seen people be very productive with these kinds of tools and able to get a lot done with them, but almost all of those people were very experienced devs that know their way around code. I am not one of those people and am able to affirm that AI should not be heavily leaned upon without a solid foundation. Let's not forget the guy who vibe coded a script to "distill" much larger models into smaller ones, that ultimately did nothing, and ended up uploading "distills" that were identical weights to their original models (yeah, you might remember me from that post). Of course ppl still ate it up, cause confirmation bias, so I guess it's all about how you market the snake oil? Either way, if you're here interested in which agentic coding tools, and models work best, read on. I will share what I've learned, including some very cool free API options at the bottom of this post. We seem to be in the boom period of agentic coding, so a lot of providers and services are being very generous. And power users of agentic coding who probably know more than me, please do comment your thoughts and experiences.
Why does it matter? You can use the best model available, or even just a mediocre model, but the tool you use with it matters. A good tool will drastically give you better results. Not only that, some models work MUCH better with specific tools. Here are my recommendations, and non-recommendations, starting with a few non-recommendations:
\- Warp: Looks like a great cli tool. Scores well in leaderboards/benchmarks, and is received well by users. BUT, no BYOK option. Makes them immediately dead on arrival as a serious option for me. You're completely at mercy to their service and any changes they make to it, randomly or not. I also don't really like the subscription model, makes little to no sense, because there's almost no transparency. You get credits to use monthly but NOWHERE do they tell you how many tokens, or requests those credits give you with any model. Their docs barely have anything on this, it's literally all vibes and doesn't tell you more than some models use more credits, and using more context, tool calls, tokens, etc use more credits.
\- Cursor: Looks like a really nice ide, and seems to work pretty well. However, suffers all the same issues as above. A lot of agentic tools do. So I wont cover too many of these. These are more like platforms + service rather than tools to use with whatever service you want.
\- Roocode: Want a quick answer? I'd probably recommend this. Very solid, all around choice. Very well recieved by the community. Has the highest rating out of all the AI extensions I saw on vscode, if that means anything. Scores very well in gosuevals (I highly suggest checking out his videos, search gosucoder on youtube, he goes very indepth in how well these agentic tools work, and in his comparisons) and is usually a top 1-3 in those monthly evals for most models. Supports code indexing for free with any provider, local api, or gemini embedding which is free via api it seems (and probably the very best embedding model available right now). Integrates well with vscode.
\- Qwen Code CLI: I don't want to make ppl read a ton to get to the best choices, so going to go ahead and share this one next because it is by far, imo, the best free, no frills option. Signup for qwen account, login via browser for oath. Done, now you have 4k qwen-coder-plus requests daily, and it's fast too at 70t/s. Qwen3 coder is one of the best opensource models, and it works way better with qwen code cli, and imo, to the point of being better than most other OSS model + tool combinations. The recent updates are very nice, adding things like planning mode. This was also imo the easiest and simplest to use of the tools ive tried. Very underrated and slept on. Qwen coder plus was originally just Qwen3 Coder 480b, the open source model, and it might still be, but they have a newer updated version that's even better, not sure if this is the one we get access too now. If it is, this easily beats using anything outside of gpt5 or claude models. this tool is gemini cli based.
\- Droid: Im still in the process of trying this one out (nothing bad yet though) so I'm going to withhold from saying too much subjective opinion and just share what I know. Scores the highest out of any agents in terminal bench so it seemed promising, but I've been looking around, and asking a lot of people about their experiences with it so far, and getting a lot of mixed feedback. I like it as a concept, will have to see if it's actually that good. Just a few anecdotal experiences are pretty unreliable after all and one big thing it has over others is that it supports BYOK at free tier without any extra caveats. The big complaint I've seen is that this tool absolutely chews through tokens (which makes their nice monthly plan less impressive), but this might not be a big deal if you use your own local model or a free api (more on this later). The most attractive thing about this tool to me is the very generous monthly plan. You get 20 million tokens for $20 monthly. Using claude sonnet uses those tokens at 1.2x, which is very nice pricing (essentially 16.7 million tokens, or around $400\~ worth of tokens based off anthropic api pricing and how much artificial analysis cost to run) when compared to the claude monthly subs (I see ppl maxing out their $100 subs at around 70 million tokens), especially when you consider its not rate limited in 5 hour periods. They also have gpt 5 codex at 0.5x (so 40 million tokens monthly), and glm 4.6 at 0.25x (80 million monthly). This is a *very* generous $20 sub imo, especially if their GLM model has thinking available (I dont think it does, which imo makes it not worth bothering to use, but the [z.ai](http://z.ai) monthly sub also has thinking disabled). I wonder if theyre eating a loss or going at cost to try and build a userbase. Lastly, they have a very nice trial, giving you 20m tokens free for one month, or 40m for 2 months if you use a referral link. I will include mine here for convenience's sake, but I do not do nearly enough AI coding to benefit from any extra credits I get so you might do someone else the favor and use their referral link instead. [https://app.factory.ai/r/0ZC7E9H6](https://app.factory.ai/r/0ZC7E9H6)
\- zed: a rust based ide. feels somewhere between a text editor like notepad++ or kate (the kde default) and vscode. its incredibly fast, and works quite well. the UI will not feel too unfamiliar from vscode, but it doesnt have the huge extensions marketplace vscode does. on the other hand, its super performant and dead simple while still feeling very full-featured, with a lot more to be added in the future. I replaced my systems default editor (kate) with zed, and have been super happy with the decision. feels much better to use. I would use it in place of vscode, but some things have better integration with vscode so I only use zed sometimes. now lets talk about it agentic capabilities. its improved a lot, and is actually near the top of gosu's latest evals. the problem is, it absolutely *chews* through tokens. same issue as droid, but even worse it seems like. They have a two week trial that gives you $20 credits. I used up $5 with sonnet 4.5 in less than a half hour. on the other hand, its byok, so I can see this being one of the best options for use with a local model, cheap api or even free api. the other thing is, I dont think there's a planning mode, or orchestrator mode, which has been the main reason I havent been using this agent. when I did test it, it absolutely overengineered everything and tried to do too much, so that might be something to watchout for as well.
\- claude code: basically the benchmark cli tool, everyone compares other tools to this tool. Has a lot of features, and was the first to have a lot of the features other agentic tools have. It's reliable and works well.
\- codex cli or vscode extension: mixed reception at first, but it's improved and ppl seem to really like it now. the gpt5 models (gpt-oss), especially codex don't really shine until used with this tool (similar to qwen coder with qwen code). The difference is very large, to the point I would say you are getting a hampered experience with those models until you use it with this tool.
\- crush: made by main dev behind opencode and charm, who has made some of the best terminal ui libraries. sounds like the dream combination right? so far it's a pretty decent all around tool, that looks really nice, but isn't anything special yet. Not a bad choice by any means. open source too.
\- gemini cli: well, the cli is nice. but gemini for whatever reason kind of sucks at agentic coding. would not bother with this until gemini 3.0 comes out. gemini 2.5 pro is whoever, still one of the best chat assistants, and an especially good for using with the research tool. if you have a student email of some sort, you can probably get a year free of gemini pro.
\- trae + seed: no byok, but looks good on swebench? sorry, im a no byok hater.
\- augment: no byok. crappy plan. doesnt even seem like its that great, better options out there.
\- refact: looks good on swebench, havent actually tried it, and doesnt seem like anyone else has really. does seem like it supports byok atleast.
\- kilocode: a novel idea, cline + roo was their main pitch, but roo has implemented most things that kilocode had, and just straight up performs better on most tasks these days. I get the feeling kilocode is just playing catchup, and only get's their once theyre upstream with roo's code since it's based off of it. some ppl still like kilocode and it can be worth using anyways if it fits your preference.
\- cline: some ppl like cline more than roo, but most prefer roo. also lower rating than roo in vscode extension store.
There are a lot more agentic coding tools out there, but I'm running out of stamina to be going through them, so next I will cover the best model options, after mentioning one important thing. Use mcp servers. They will enhance your agentic coding by a lot. I highly suggest at least getting the likes of exa search, context7, etc. I haven't used very many of these yet and am in the process of experimenting with them, so I cant offer too much advice here (thankfully. Im writing way too much.)
The very best model right now, for agentic coding, is sonnet 4.5. This will probably change at some point so do some research if this post isnt recent anymore. Only gpt 5 codex comes close or is as good, and thats only if you use it with codex cli or the codex extension. These options can however be a little pricy, especially if you pay by the token in api cost. The monthly subs however, can be worth it to some. Afterall, sometimes it much better to get things done in one shot than spend hours reprompting, rolling back changes and trying again with a lesser model.
The next tier of models is pretty interesting. None of these come very close to the top two choices, but are all relatively close to each other in capability, regardless of cost. Gpt-5, the non codex model is one such model, and probably near the top of this tier, but it costs the same as gpt-5 codex so why would you use it? The best bang for buck model in this category is probably gpt 5 mini (medium reasoning, high reasoning isnt much better and takes up a lot more tokens), and deepseek v3.2-exp, if we go based purely of cost per token. gpt 5 mini is more capable, but a little more expensive. Deepseek v3.2 is by far the cheapest of this category, and surprisingly capable for how cheap it is, I would rate it just under kimi k2 0905 and qwen3 coder 480b. GLM 4.6 is only around those two mentioned models with reasoning disabled, but with reasoning enabled it becomes much better. Sadly, the glm sub that everyone has been so hyped about, has thinking disabled. So get the sub if you want.. it is cheap as heck, but.. know you are only getting around that level of capability. Here's where it gets interesting. Gpt 5 mini is completely free with copilot pro, which is also free if you have any old (or current) student email. This, with reasoning at medium is step above glm 4.6 without reasoning. Unfortunately you do get tied down to using it within copilot, or tools that have custom headers to spoof their agent built-in (I think opencode has this?). Now for the free models.. kimi k2 0905 is completely free, unlimited use at 40 rpm, via the nvidia nim api. just make an account and get an api key, use like any other openai compatible api. This is by far the best or one of the best non-thinking models. It's in the same realm as glm 4.6 without reasoning (above it slightly I'd say, but glm 4.6 with reasoning will blow it out), qwen coder 480b (above it slightly I'd say, unless used with qwen code, where I then give the edge to qwen coder). GLM 4.6, if reasoning is enabled is near the top of this pack, but this tier of models is still significantly below the best one or two models.
A note on roocode, and other providers that support code indexing via embedding models. roo specifically supports gemini embedding which is bar none the very best available, and is apparently completely free via api atm. but if your tool doesnt support it, nebiusai gives you $1 credit for free on signup, that never expires afaik, and their qwen3 embedding 8b model is the cheapest of any provider at 0.01 per million. That $1 will last you forever if you use it for embedding only, and it is the second best available embedding model behind gemini (and is the very best OSS embedding model atm). sadly they dont have any reranking models, but I think I only saw one tool that supported this? and cant remember which tool it is. if you do stumble across one, you can sign up with novita for a $1 voucher as well, and use qwen3 reranker 8b from their api. Pretty good combo on roo code, to use kimi k2 0905 from nvidia api, and either gemini embedding or nebius' qwen3 embedding.
As far as local models go for running on typical home computers, these unfortunately, have a very big gap between much larger OSS models, that youre better off using off a free api, or trial credits, but if you dont care enough to, or are just trying stuff for fun, privacy, etc, your best bets are qwen3 coder 30b a3b with qwen code cli, or gpt-oss 20b + codex cli/extension. next step up is gpt oss 120b with codex cli/extension if you have the ram and vram for it. Devstral small 2507 is okay too, but I dont think its quite as good for its size.
Lastly, speaking on free credits, I came across some reddit posts claiming free credits for some chinese openrouter clone looking website called agent router. Was extremely sussed out by it, and couldnt find much information on it other than few ppl saying they got it working after some hassle, and that the software stack is based off a real opensource stack with repos available on github (new api and one api). Decided to very reluctantly give it a shot, but the website was a buggy half implemented mess throwing backend errors galore, which sussed me out more. They only supported signup via oath from github and linux do. Me wondering what the catch was, checked my permissions after signing up with github, and saw they only got read access to what email my github was under. I saw I did get my credits from signing up via referral. The rates for sonnet looked typical, but the rates for the other models seemed too good to be true. So I get an api key, try it with my pageassist firefox extension (I highly recommend it, dev is great, has added a bunch of stuff after feedback on discord), and got 401 error. Tried with cherry studio (also very nice), same error. Website has me logged out now, and I cant log back in, I keep getting error too many requests in chinese. Gave up. Tried again daily for a few days and same issues. Finally, today the website is working perfectly, no lag either. Im amazed, was starting to think it was some sort of weird scam, which is why I hadnt told anyone about it yet. Says I have no api keys for some reason so I make a new one. doesnt work still. after some replies from other on reddit, and reading the docs, I realize, these models only work with specific tools, so that seems to be the main catch. after realizing this I reinstalled codex cli, followed the docs for using the api with codex cli (this is a must btw) after translating with deepseek v3.2 and it was working perfectly. Mind blown. So now I have $125 credits with temu openrouter, which serves gpt 5 at only 0.003 dollars per million tokens lol. Me and a few others have a sneaking suspicion the hidden catch is that they store, and use your data, probably for training, but personally I dont care. If this isnt an issue for you guys either, I highly suggest finding someone's referral link and using it to signup with github or linuxdo. You will get $100 from the referral, and $25 for logging in. Again, I still have my trial credits through from other tools, and dont use ai coding much so use someone elses referral if you wanna be nice, but I will throw mine in here anyways for convenience sake. [https://agentrouter.org/register?aff=ucNl](https://agentrouter.org/register?aff=ucNl) PS I suggest using a translation tool as not all of it is in english, I used the first ai translation extension that works with openrouter I found from the firefox store lol.
On a second read, maybe I should have put this through some ai to make this more human readable. Ah well. I bet one of you will put this through claude sonnet anyways, and comment it below. wont be me though. Tl;dr if you skipped to the bottom though; nvidia nim api is free, use kimi k2 0905 from there with any tool that looks interesting, roo code is the all round solid choice. or just use qwen code cli with oath.
some links:
[https://build.nvidia.com/explore/discover](https://build.nvidia.com/explore/discover)
[https://gosuevals.com/](https://gosuevals.com/)
[https://www.youtube.com/gosucoder](https://www.youtube.com/gosucoder) (no im not affaliated with him, or anything/anyone mentioned in this post)
[https://discord.com/invite/YGS4AJ2MxA](https://discord.com/invite/YGS4AJ2MxA) (his discord, I hang out here and the koboldai discord a lot if you wanna find me)
[https://github.com/QwenLM/qwen-code](https://github.com/QwenLM/qwen-code)
[https://github.com/upstash/context7](https://github.com/upstash/context7) | 2025-10-15T10:26:15 | https://www.reddit.com/r/LocalLLaMA/comments/1o77ag4/a_guide_to_the_best_agentic_tools_and_the_best/ | lemon07r | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o77ag4 | false | null | t3_1o77ag4 | /r/LocalLLaMA/comments/1o77ag4/a_guide_to_the_best_agentic_tools_and_the_best/ | false | false | self | 39 | null |
Sam Ctrl Altman | 1 | 2025-10-15T10:12:12 | Substantial-Gas-5735 | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1o771wf | false | null | t3_1o771wf | /r/LocalLLaMA/comments/1o771wf/sam_ctrl_altman/ | false | false | default | 1 | {'enabled': True, 'images': [{'id': '3h33khc539vf1', 'resolutions': [{'height': 84, 'url': 'https://preview.redd.it/3h33khc539vf1.jpeg?width=108&crop=smart&auto=webp&s=9705ee393aa1a3e0f4f044b6dae08c3659eefebf', 'width': 108}, {'height': 169, 'url': 'https://preview.redd.it/3h33khc539vf1.jpeg?width=216&crop=smart&auto=webp&s=3d129f77650c84daeb73b9b66a510e1cefb72471', 'width': 216}, {'height': 250, 'url': 'https://preview.redd.it/3h33khc539vf1.jpeg?width=320&crop=smart&auto=webp&s=35a87c3e3c150cd530434f109938a66a3b012731', 'width': 320}, {'height': 500, 'url': 'https://preview.redd.it/3h33khc539vf1.jpeg?width=640&crop=smart&auto=webp&s=7eda598d481524c9a1188766e3612fac72727396', 'width': 640}, {'height': 751, 'url': 'https://preview.redd.it/3h33khc539vf1.jpeg?width=960&crop=smart&auto=webp&s=1140f5f2cfd93ef2e1524aacb8b043940e6317ba', 'width': 960}, {'height': 845, 'url': 'https://preview.redd.it/3h33khc539vf1.jpeg?width=1080&crop=smart&auto=webp&s=c391dec619b449df063acbc74c0e0a014e68b51b', 'width': 1080}], 'source': {'height': 917, 'url': 'https://preview.redd.it/3h33khc539vf1.jpeg?auto=webp&s=bb0a9b95e254f2f31735d3778fe70ff550fe78ce', 'width': 1172}, 'variants': {}}]} | ||
Sam Ctrl Altman | 1 | [deleted] | 2025-10-15T10:11:18 | [deleted] | 1970-01-01T00:00:00 | 0 | {} | 1o771bu | false | null | t3_1o771bu | /r/LocalLLaMA/comments/1o771bu/sam_ctrl_altman/ | false | false | default | 1 | null | ||
Practical OCR with Nanonets OCR2‑3B | 22 | It’s pleasantly low-friction. I used to write dozens of lines of regex to scrape multi-level headers in financial reports; now OCR2‑3B gives me a decent Markdown table, and I just straighten amount columns and unify units, my hours got cut in half. For papers, title/author/abstract come out clean, references are mostly structured; dedup is all that’s left. I don’t trust contracts 100%, but clause hierarchies show up; searching for “indemnity/termination/cancellation” beats flipping through PDFs.
Failure modes I hit: if a page has Subtotal/Tax/Total, it sometimes labels Subtotal as Total; in heavily compressed scans, “8.” turns into “B.” Handwritten receipts are still hard—skewed and blurry ones won’t magically fix themselves.
If you want to try it, I’d do this: don’t over-compress images; keep the long edge ≥ 1280px. In the prompt, specify tables in Markdown and keep formulas as $...$, it helps a lot. If you stitch many receipts into a tall image, localization degrades; it may “imagine” headers span across receipts. Feed single receipts one by one and the success rate comes back.
HF: [https://huggingface.co/nanonets/Nanonets-OCR2-3B](https://huggingface.co/nanonets/Nanonets-OCR2-3B) | 2025-10-15T09:51:40 | https://www.reddit.com/r/LocalLLaMA/comments/1o76pft/practical_ocr_with_nanonets_ocr23b/ | Gold-Cup8831 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o76pft | false | null | t3_1o76pft | /r/LocalLLaMA/comments/1o76pft/practical_ocr_with_nanonets_ocr23b/ | false | false | self | 22 | {'enabled': False, 'images': [{'id': 'VTv2TZSFyHLlE7HiuxqbwivJABaijwptEaFk0L9z-fw', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/VTv2TZSFyHLlE7HiuxqbwivJABaijwptEaFk0L9z-fw.png?width=108&crop=smart&auto=webp&s=c354bf456e96ffcb22faf45448e30c27e3f84b00', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/VTv2TZSFyHLlE7HiuxqbwivJABaijwptEaFk0L9z-fw.png?width=216&crop=smart&auto=webp&s=3bb0e9047fb50f0dbb60c49a6a2b7737cf659a41', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/VTv2TZSFyHLlE7HiuxqbwivJABaijwptEaFk0L9z-fw.png?width=320&crop=smart&auto=webp&s=a54c9578febd7d445c355461fdc2fe7a17818aa2', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/VTv2TZSFyHLlE7HiuxqbwivJABaijwptEaFk0L9z-fw.png?width=640&crop=smart&auto=webp&s=dc905707914e7a4b5a771921ed4332e09c8d0d89', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/VTv2TZSFyHLlE7HiuxqbwivJABaijwptEaFk0L9z-fw.png?width=960&crop=smart&auto=webp&s=6a29648657483bfbd65ca43508d8cbe23ae7307b', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/VTv2TZSFyHLlE7HiuxqbwivJABaijwptEaFk0L9z-fw.png?width=1080&crop=smart&auto=webp&s=f68c0c6468750b05f40f80120756cf064d7d310b', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/VTv2TZSFyHLlE7HiuxqbwivJABaijwptEaFk0L9z-fw.png?auto=webp&s=41ea271a5b6684d11f4c355fc6cbb01d0bbadc14', 'width': 1200}, 'variants': {}}]} |
I built an AI orchestration platform that breaks your promot and runs GPT-5, Claude Opus 4.1, Gemini 2.5 Pro, and 17+ other models together - with an Auto-Router that picks the best approach | 0 | Hey everyone! I've been frustrated with choosing between AI models - GPT-5 is great at reasoning, Claude excels at creative writing, Gemini handles data well, Perplexity is best for research - so I built LLM Hub to orchestrate them all intelligently.
🎯 **The Core Problem:** Each AI has strengths and weaknesses. Using just one means compromising on quality.
💡 **The Solution:** LLM Hub coordinates 20+ models across 4 execution modes:
**4 EXECUTION MODES:**
**Single Mode** \- One model, one response (traditional chat)
**Sequential Mode** \- Chain models where each builds on the previous (research → analysis → writing)
**Parallel Mode** \- Multiple models tackle the same task, synthesized by a judge model
🌟 **Specialist Mode** (the game-changer) - Breaks complex tasks into up to 4 specialized segments, routes each to the expert model, runs them in parallel, then synthesizes everything
**🧠 AUTO-ROUTING ENGINE:**
Instead of you guessing which mode to use, the AI analyzes your prompt through 14 analytical steps:
* **Complexity Analysis** (1-10 scale): Word count, sentence structure, technical depth, multi-step detection
* **Content Type Detection:** Code, research, creative, analysis, data, reasoning, math
* **Context Requirements:** Needs web search? Deep reasoning? Multiple perspectives? Vision capabilities?
* **Multi-Domain Detection:** Does this need code + research + creative all together?
* **Quality Optimization:** Balance between speed and output quality
* **Language Detection:** Translates non-English prompts automatically for routing
Based on this analysis, it automatically selects:
* Which execution mode (single/sequential/parallel/specialist)
* Which specific models to use
* Whether to enable web browsing (Perplexity Sonar integration)
* Whether to use image/video generation
* Optimal synthesis strategy
**Example routing decisions:**
* Simple question (complexity 2) → Single mode with GPT-5-mini
* Complex analysis (complexity 7) → Parallel mode with GPT-5, Claude Sonnet 4.5, Gemini 2.5 Pro + judge
* Multi-domain task (complexity 8) → Specialist Mode with 3-4 segments
**🌟 SPECIALIST MODE DEEP DIVE:**
This is where it gets powerful. When you ask something like:
*"Build a web scraper to analyze competitor pricing, then create a marketing report with data visualizations"*
Specialist Mode:
1. **Segments the task** (using GPT-4o-mini for fast decomposition):
* Segment 1: Python web scraping code → Routed to Claude Sonnet 4.5 (best at code)
* Segment 2: Pricing analysis → Routed to Claude Opus 4.1 (best at analysis)
* Segment 3: Marketing report → Routed to GPT-5 (best at creative + business writing)
* Segment 4: Data visualization → Routed to Gemini 2.5 Pro (best at data processing)
2. **Executes all segments in parallel** (simultaneous, not sequential)
3. **Synthesizes outputs** using GPT-5-mini (fast, high-context synthesis)
**Result:** You get expert-level output in each domain, finished faster than sequential processing.
**🔧 OTHER KEY FEATURES:**
* **Visual Workflow Builder:** Drag-and-drop automation with 10+ node types (prompt, condition, loop, export, etc.) + AI-generated workflows
* **Scheduled Workflows:** Cron-based automation for recurring tasks
* **Multi-Modal:** DALL-E 3, Nano Banana (Gemini Image), Sora 2, Veo 2 for image/video generation
* **Real-Time Web Search:** Perplexity Sonar Pro integration
* **Advanced Analytics:** Track usage, model performance, compare results
* **Export Everything:** JSON, CSV, Excel, Word, PDF
**Try it:** [https://llm-hub.tech](https://llm-hub.tech/)
Would love feedback! Especially from ML engineers - curious if anyone's tackled similar routing optimization problems. | 2025-10-15T09:47:48 | https://www.reddit.com/r/LocalLLaMA/comments/1o76n9d/i_built_an_ai_orchestration_platform_that_breaks/ | No_Pizza_8952 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o76n9d | false | null | t3_1o76n9d | /r/LocalLLaMA/comments/1o76n9d/i_built_an_ai_orchestration_platform_that_breaks/ | false | false | self | 0 | null |
Reproducing Karpathy’s NanoChat on a Single GPU — Step by Step with AI Tools | 8 | AI tools can now rebuild entire repos into runnable notebooks.
I used DeepWiki + Gemini to reproduce Karpathy’s *NanoChat* in a single Colab notebook running on one GPU. $0 spent.
Read the full story 👇
[https://limcheekin.medium.com/reproducing-karpathys-nanochat-on-a-single-gpu-step-by-step-with-ai-tools-e9420aaee912](https://limcheekin.medium.com/reproducing-karpathys-nanochat-on-a-single-gpu-step-by-step-with-ai-tools-e9420aaee912)
Appreciate any feedback from you. | 2025-10-15T09:32:52 | https://limcheekin.medium.com/reproducing-karpathys-nanochat-on-a-single-gpu-step-by-step-with-ai-tools-e9420aaee912 | Fresh-Recover1552 | limcheekin.medium.com | 1970-01-01T00:00:00 | 0 | {} | 1o76ev6 | false | null | t3_1o76ev6 | /r/LocalLLaMA/comments/1o76ev6/reproducing_karpathys_nanochat_on_a_single_gpu/ | false | false | default | 8 | {'enabled': False, 'images': [{'id': '2nIcOLn_4UHZKPN7434gGUG3AWhQ6tAHEqr0xSwVurg', 'resolutions': [{'height': 60, 'url': 'https://external-preview.redd.it/2nIcOLn_4UHZKPN7434gGUG3AWhQ6tAHEqr0xSwVurg.png?width=108&crop=smart&auto=webp&s=a4cdbf0f8769fbb48d7a6f412426ca51761eea3a', 'width': 108}, {'height': 121, 'url': 'https://external-preview.redd.it/2nIcOLn_4UHZKPN7434gGUG3AWhQ6tAHEqr0xSwVurg.png?width=216&crop=smart&auto=webp&s=d1f989700137b5b206e78b829603a0967e71d565', 'width': 216}, {'height': 180, 'url': 'https://external-preview.redd.it/2nIcOLn_4UHZKPN7434gGUG3AWhQ6tAHEqr0xSwVurg.png?width=320&crop=smart&auto=webp&s=c6acf5a12141772b9e163061f3ba0644a3f97d1b', 'width': 320}, {'height': 360, 'url': 'https://external-preview.redd.it/2nIcOLn_4UHZKPN7434gGUG3AWhQ6tAHEqr0xSwVurg.png?width=640&crop=smart&auto=webp&s=c96115f1ddf09012f32682be6f927394354d91df', 'width': 640}, {'height': 540, 'url': 'https://external-preview.redd.it/2nIcOLn_4UHZKPN7434gGUG3AWhQ6tAHEqr0xSwVurg.png?width=960&crop=smart&auto=webp&s=0efaae8fbbc4cc09d53466bd47054c8431dcc2c4', 'width': 960}, {'height': 607, 'url': 'https://external-preview.redd.it/2nIcOLn_4UHZKPN7434gGUG3AWhQ6tAHEqr0xSwVurg.png?width=1080&crop=smart&auto=webp&s=8321744698f6c4e6207f04b0903fa920f68ce17e', 'width': 1080}], 'source': {'height': 675, 'url': 'https://external-preview.redd.it/2nIcOLn_4UHZKPN7434gGUG3AWhQ6tAHEqr0xSwVurg.png?auto=webp&s=1fc44483ad086e435dc5595dee203bfacd56939e', 'width': 1200}, 'variants': {}}]} |
🧠 The “new RAG every week” problem — do we really need to switch frameworks this often? | 0 | It feels like every couple of weeks, there’s a *brand-new* “next-generation” RAG framework popping up — **R2R**, **LightRAG**, **RAGFlow**, and now a few others that just launched on GitHub.
But when you look closer, most of them aren’t *new paradigms* at all.
They’re slight variations of existing pipelines — same retrieval backbone, same chunking strategies — maybe with a bit of optimization in one step (say, hybrid retriever logic, graph-based context linking, or compression on long contexts).
From a research and engineering standpoint, this raises a question that’s been bothering me lately:
**At what point is it worth switching frameworks?**
Every time a new framework appears:
* My embeddings need to be regenerated,
* My indexes rebuilt,
* My evaluation baselines invalidated.
Yet the claimed performance improvements are often marginal — sometimes within 1–2% accuracy or even within noise range, and often not tested on the same datasets.
I get that incremental progress matters, but the ecosystem is starting to fragment around “localized optimizations” without unified evaluation.
We’re reinventing the same RAG cycle — chunk, retrieve, rerank, generate — just with slightly different wrappers.
What I’d *love* to see is:
* A **standardized RAG benchmark** where frameworks can be compared under identical datasets and metrics.
* Clear reporting of *which aspect* each framework actually improves (retrieval precision, context recall, latency, etc.) rather than vague “better results”.
* Some guidance for deciding **when it’s actually worth migrating** your existing data pipeline.
So I’m genuinely curious —
Has anyone here done a *controlled comparison* between LightRAG / R2R / RAGFlow (or newer ones)?
Did you see meaningful gains that justify a full migration?
Or are we just chasing architectures that differ more in packaging than in principle? | 2025-10-15T09:14:41 | https://www.reddit.com/r/LocalLLaMA/comments/1o764lj/the_new_rag_every_week_problem_do_we_really_need/ | Cheryl_Apple | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o764lj | false | null | t3_1o764lj | /r/LocalLLaMA/comments/1o764lj/the_new_rag_every_week_problem_do_we_really_need/ | false | false | self | 0 | null |
ONNX Speech Models: What's Your Favorite? (A Quick Poll) | 1 | [removed] | 2025-10-15T09:08:51 | https://www.reddit.com/r/LocalLLaMA/comments/1o761dy/onnx_speech_models_whats_your_favorite_a_quick/ | isuite-dev | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o761dy | false | null | t3_1o761dy | /r/LocalLLaMA/comments/1o761dy/onnx_speech_models_whats_your_favorite_a_quick/ | false | false | self | 1 | null |
Anyone else having reasoning parser issue with Qwen-cli + GLM4.6 combo in vllm? | 6 | 2025-10-15T08:52:11 | https://www.reddit.com/r/LocalLLaMA/comments/1o75s9p/anyone_else_having_reasoning_parser_issue_with/ | kyazoglu | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o75s9p | false | null | t3_1o75s9p | /r/LocalLLaMA/comments/1o75s9p/anyone_else_having_reasoning_parser_issue_with/ | false | false | 6 | null | ||
How can I enable LLM running on my remote Ollama server to access the local files? | 0 | I want to create the following setup: a local AI CLI Agent that can access files on my system and use bash (for example, to analyze a local SQLite database). That agent should communicate with my remote Ollama server hosting LLMs.
Currently, I can chat with LLM on the Ollama server via the AI CLI Agent.
When I try to make the AI Agent analyze local files, I sometimes get
`AI_APICallError: Not Found`
and, most of the time, the agent is totally lost:
'We see invalid call. Need to read file content; use filesystem_read_text_file. We'll investigate code.We have a project with mydir and modules/add. likely a bug. Perhaps user hasn't given a specific issue yet? There is no explicit problem statement. The environment root has tests. Probably the issue? Let me inspect repository structure.Need a todo list? No. Let's read directory.{"todos":"'}'
I have tried the server-filesystem MCP, but it hasn't improved anything.
At the same time, the Gemini CLI works perfectly fine - it can browse local files and use bash to interact with SQLite.
How can I improve my setup? I have tested nanocoder and opencode AI CLI agents - both have the same issues when working with remote GPT-OSS-20B. Everything works fine when I connect those AI Agents to Ollama running on my laptop - the same agents can interact with the local filesystem backed by the same LLM in the local Ollama.
How can I replicate those capabilities when working with remote Ollama? | 2025-10-15T08:50:18 | ThingRexCom | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1o75r7q | false | null | t3_1o75r7q | /r/LocalLLaMA/comments/1o75r7q/how_can_i_enable_llm_running_on_my_remote_ollama/ | false | false | default | 0 | {'enabled': True, 'images': [{'id': '82q15rgdo8vf1', 'resolutions': [{'height': 45, 'url': 'https://preview.redd.it/82q15rgdo8vf1.png?width=108&crop=smart&auto=webp&s=321b311d8448ab437d348ade2fc9b2e86781ac89', 'width': 108}, {'height': 90, 'url': 'https://preview.redd.it/82q15rgdo8vf1.png?width=216&crop=smart&auto=webp&s=aaa76a26be0dd3787a86fa5a58ed278935a97f8d', 'width': 216}, {'height': 134, 'url': 'https://preview.redd.it/82q15rgdo8vf1.png?width=320&crop=smart&auto=webp&s=9644b2b58d8317c4fc183360fd7032f06ad33e0d', 'width': 320}, {'height': 268, 'url': 'https://preview.redd.it/82q15rgdo8vf1.png?width=640&crop=smart&auto=webp&s=aa75ba79b9da9cf9819105597f80cd3dc14829ab', 'width': 640}, {'height': 402, 'url': 'https://preview.redd.it/82q15rgdo8vf1.png?width=960&crop=smart&auto=webp&s=62832a78dfa288377840395931c0405691be067f', 'width': 960}, {'height': 452, 'url': 'https://preview.redd.it/82q15rgdo8vf1.png?width=1080&crop=smart&auto=webp&s=1308d68eaa92d338ad7895dc9fe3dbe23c2ca224', 'width': 1080}], 'source': {'height': 1234, 'url': 'https://preview.redd.it/82q15rgdo8vf1.png?auto=webp&s=5d32fc454f0c7d0a1f618dad0c2d4020cb7abcd9', 'width': 2942}, 'variants': {}}]} | |
AI has replaced programmers… totally. | 1,235 | 2025-10-15T08:37:54 | jacek2023 | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1o75kkb | false | null | t3_1o75kkb | /r/LocalLLaMA/comments/1o75kkb/ai_has_replaced_programmers_totally/ | false | false | default | 1,235 | {'enabled': True, 'images': [{'id': 'bnnb2fb9m8vf1', 'resolutions': [{'height': 108, 'url': 'https://preview.redd.it/bnnb2fb9m8vf1.png?width=108&crop=smart&auto=webp&s=f6b42737f4b4277787f05ed4372397390b13c0df', 'width': 108}, {'height': 216, 'url': 'https://preview.redd.it/bnnb2fb9m8vf1.png?width=216&crop=smart&auto=webp&s=719c065918015770f3f22067869ef2108fba2fb8', 'width': 216}, {'height': 320, 'url': 'https://preview.redd.it/bnnb2fb9m8vf1.png?width=320&crop=smart&auto=webp&s=7bea7ac109da9399fa44269a3751e9ff7d5e4a56', 'width': 320}, {'height': 640, 'url': 'https://preview.redd.it/bnnb2fb9m8vf1.png?width=640&crop=smart&auto=webp&s=e1a55140b6915df726dfa4932943df64e43e7d94', 'width': 640}, {'height': 960, 'url': 'https://preview.redd.it/bnnb2fb9m8vf1.png?width=960&crop=smart&auto=webp&s=d394b6ddb7a5ab63e14bbe807fe4f3e903c7afa1', 'width': 960}], 'source': {'height': 1024, 'url': 'https://preview.redd.it/bnnb2fb9m8vf1.png?auto=webp&s=2a14f8efceefc08702cda5c56599445c0a50742b', 'width': 1024}, 'variants': {}}]} | ||
Why choose DGX Spark over Framework Desktop (or Mac Studio!) | 13 | After watching a few reviews it's clear that DGX Spark inference performance is a little bit disappointing, but [the review at Level1Techs in YouTube](https://www.youtube.com/watch?v=Lqd2EuJwOuw) is insightful. It shows how hardware support for NVFP4 makes the machine compensate its memory banwidth limitations and also makes the Spark interesting as a way to scale to the CDNA GPU NVIDIA Fabric.
I understand that, but for a user that just wants to run local models, I find the Framework Desktop cheaper and quite interesting (I know, Vulcan, not CUDA) to run big models, and I find the Mac Studio or some MacBook Pro M4 Max even more interesting to run big models with a good token/s performance.
What am I missing here? For me DGX Spark is meh even with its ecosystem, so... is that so important? | 2025-10-15T08:37:22 | https://www.reddit.com/r/LocalLLaMA/comments/1o75ka2/why_choose_dgx_spark_over_framework_desktop_or/ | javipas | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o75ka2 | false | null | t3_1o75ka2 | /r/LocalLLaMA/comments/1o75ka2/why_choose_dgx_spark_over_framework_desktop_or/ | false | false | self | 13 | {'enabled': False, 'images': [{'id': '7RK2-ldRyeO1G7cH7WvYJ5T5vh_Eu5lzsFNonawEwKQ', 'resolutions': [{'height': 81, 'url': 'https://external-preview.redd.it/7RK2-ldRyeO1G7cH7WvYJ5T5vh_Eu5lzsFNonawEwKQ.jpeg?width=108&crop=smart&auto=webp&s=4d026f27e33bfa63f7d03faa562cc7fe208e45d3', 'width': 108}, {'height': 162, 'url': 'https://external-preview.redd.it/7RK2-ldRyeO1G7cH7WvYJ5T5vh_Eu5lzsFNonawEwKQ.jpeg?width=216&crop=smart&auto=webp&s=162e027ee2ada69089716b9d3823f8bafdb4795b', 'width': 216}, {'height': 240, 'url': 'https://external-preview.redd.it/7RK2-ldRyeO1G7cH7WvYJ5T5vh_Eu5lzsFNonawEwKQ.jpeg?width=320&crop=smart&auto=webp&s=a2bc255a8fb6796dcc94e6bb0546062a3188356c', 'width': 320}], 'source': {'height': 360, 'url': 'https://external-preview.redd.it/7RK2-ldRyeO1G7cH7WvYJ5T5vh_Eu5lzsFNonawEwKQ.jpeg?auto=webp&s=9582514b01a44058a5013d306d02dfdf4dd1de58', 'width': 480}, 'variants': {}}]} |
What factors will widen or close the perf gap between between open and close source LLMs? | 3 | So i was reading this article [https://epoch.ai/blog/open-models-report#c-compute-regression-analysis](https://epoch.ai/blog/open-models-report#c-compute-regression-analysis) and wondering if the performance gap between open and closed source models will increase or decrease in the future? By adding more recent data, the gap seems to closing on textual and mathematical reasoning tasks but remain present for high-context & multimodal reasoning. Is this difference in tasks because 1. open-source models distill responses from closed model into training sets, increasing accuracy on textual and mathematical reasoning tasks and 2. Closed models have higher training compute resources, important for high-context & multimodal reasoning? Is that all there is to it or am I missing something? | 2025-10-15T08:29:26 | https://www.reddit.com/r/LocalLLaMA/comments/1o75fxe/what_factors_will_widen_or_close_the_perf_gap/ | Sir-Earl-Grey | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o75fxe | false | null | t3_1o75fxe | /r/LocalLLaMA/comments/1o75fxe/what_factors_will_widen_or_close_the_perf_gap/ | false | false | self | 3 | {'enabled': False, 'images': [{'id': '0U5PN2fYF9OsnR_qgZo2_L9ZNAJp8JSaNKo_L5HVXI4', 'resolutions': [{'height': 60, 'url': 'https://external-preview.redd.it/0U5PN2fYF9OsnR_qgZo2_L9ZNAJp8JSaNKo_L5HVXI4.png?width=108&crop=smart&auto=webp&s=5213904a67333d8a90df1a96f3aa68d67476ec68', 'width': 108}, {'height': 121, 'url': 'https://external-preview.redd.it/0U5PN2fYF9OsnR_qgZo2_L9ZNAJp8JSaNKo_L5HVXI4.png?width=216&crop=smart&auto=webp&s=906bc657fe91e90ed99ad8ae2fe8490866ed7d88', 'width': 216}, {'height': 180, 'url': 'https://external-preview.redd.it/0U5PN2fYF9OsnR_qgZo2_L9ZNAJp8JSaNKo_L5HVXI4.png?width=320&crop=smart&auto=webp&s=ff26819ec1ac1147005daf090789295d2a869ffe', 'width': 320}, {'height': 360, 'url': 'https://external-preview.redd.it/0U5PN2fYF9OsnR_qgZo2_L9ZNAJp8JSaNKo_L5HVXI4.png?width=640&crop=smart&auto=webp&s=f5491bcc66f28e32c58ed76353a0c9aec8f475eb', 'width': 640}, {'height': 540, 'url': 'https://external-preview.redd.it/0U5PN2fYF9OsnR_qgZo2_L9ZNAJp8JSaNKo_L5HVXI4.png?width=960&crop=smart&auto=webp&s=98f9a50f326408485f0486c7c57955636e53083f', 'width': 960}, {'height': 608, 'url': 'https://external-preview.redd.it/0U5PN2fYF9OsnR_qgZo2_L9ZNAJp8JSaNKo_L5HVXI4.png?width=1080&crop=smart&auto=webp&s=56c93044db2d0ffb1dd45848a8a9a361558baab4', 'width': 1080}], 'source': {'height': 1520, 'url': 'https://external-preview.redd.it/0U5PN2fYF9OsnR_qgZo2_L9ZNAJp8JSaNKo_L5HVXI4.png?auto=webp&s=9ad7eb336fd8c545345a73a97887f8faab92087e', 'width': 2700}, 'variants': {}}]} |
best local model for article analysis and summarization | 9 | i’m early in my testing journey of determining the best local model for my use case.
in this particular instance i’m trying to find a local model that can ingest article data and output structured responses around key points, impact analysis, and things of that nature.
is there a model that you think would best suit this kind of work? | 2025-10-15T08:20:53 | https://www.reddit.com/r/LocalLLaMA/comments/1o75bah/best_local_model_for_article_analysis_and/ | Luke1144 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o75bah | false | null | t3_1o75bah | /r/LocalLLaMA/comments/1o75bah/best_local_model_for_article_analysis_and/ | false | false | self | 9 | null |
Context Engineering = Information Architecture for LLMs | 3 | Hey guys,
I wanted to share an interesting insight about context engineering. At Innowhyte, our motto is **Driven by Why, Powered by Patterns.** This thinking led us to recognize the principles that solve information overload for humans also solve attention degradation for LLMs. We feel certain principles of Information Architecture are very relevant for Context Engineering.
In our latest blog, we break down:
* **Why long contexts fail** \- Not bugs, but fundamental properties of transformer architecture, training data biases, and evaluation misalignment
* **The real failure modes** \- Context poisoning, history weight, tool confusion, and self-conflicting reasoning we've encountered in production
* **Practical solutions mapped to Dan Brown's IA principles -** We show how techniques like RAG, tool selection, summarization, and multi-agent isolation directly mirror established information architecture principles from UX design
The gap between "this model can do X" and "this system reliably does X" is information architecture (context engineering). Your model is probably good enough. Your context design might not be.
Read the full breakdown in our latest blog: [why-context-engineering-mirrors-information-architecture-for-llms](http://www.innowhyte.ai/blogs/why-context-engineering-mirrors-information-architecture-for-llms). Please share your thoughts, whether you agree or disagree. | 2025-10-15T08:17:45 | https://www.reddit.com/r/LocalLLaMA/comments/1o759mr/context_engineering_information_architecture_for/ | shivmohith8 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o759mr | false | null | t3_1o759mr | /r/LocalLLaMA/comments/1o759mr/context_engineering_information_architecture_for/ | false | false | self | 3 | {'enabled': False, 'images': [{'id': 'LmQkr9XLxKaq9PAjFz7KbpkOZNGaO2zGJ02RtSlGhzU', 'resolutions': [{'height': 46, 'url': 'https://external-preview.redd.it/LmQkr9XLxKaq9PAjFz7KbpkOZNGaO2zGJ02RtSlGhzU.png?width=108&crop=smart&auto=webp&s=54a693203cb1e8ca6ceba7fb42ae91d24f1a4988', 'width': 108}, {'height': 92, 'url': 'https://external-preview.redd.it/LmQkr9XLxKaq9PAjFz7KbpkOZNGaO2zGJ02RtSlGhzU.png?width=216&crop=smart&auto=webp&s=a128510c16a389e570c54aebcb62db3930f25428', 'width': 216}, {'height': 136, 'url': 'https://external-preview.redd.it/LmQkr9XLxKaq9PAjFz7KbpkOZNGaO2zGJ02RtSlGhzU.png?width=320&crop=smart&auto=webp&s=450555bed6232ae0ece61d781dd3b1a6c1de8ac0', 'width': 320}, {'height': 272, 'url': 'https://external-preview.redd.it/LmQkr9XLxKaq9PAjFz7KbpkOZNGaO2zGJ02RtSlGhzU.png?width=640&crop=smart&auto=webp&s=197fff9f61358ed4320d3de30100d306de6d1902', 'width': 640}, {'height': 409, 'url': 'https://external-preview.redd.it/LmQkr9XLxKaq9PAjFz7KbpkOZNGaO2zGJ02RtSlGhzU.png?width=960&crop=smart&auto=webp&s=082bbed2add3dd7e015ffbb6ea95cda802e156c2', 'width': 960}, {'height': 460, 'url': 'https://external-preview.redd.it/LmQkr9XLxKaq9PAjFz7KbpkOZNGaO2zGJ02RtSlGhzU.png?width=1080&crop=smart&auto=webp&s=274c75771f900ad2802764e3bbd81985591b2744', 'width': 1080}], 'source': {'height': 597, 'url': 'https://external-preview.redd.it/LmQkr9XLxKaq9PAjFz7KbpkOZNGaO2zGJ02RtSlGhzU.png?auto=webp&s=9c9d2336876125b1d367a2e865d460ced3fe8566', 'width': 1400}, 'variants': {}}]} |
My first 15 days with GLM-4.6 — honest thoughts after using Opus and Sonnet | 110 | When I first subscribed and started using **GLM-4.6** with **KiloCode**, I was honestly a bit disappointed. I had gotten used to the kind of UI/UX-focused results I was getting from **Opus 4.1** and **Sonnet**, and GLM felt different at first.
But after a couple of weeks of real use, I’ve started to really appreciate it. For **pure programming tasks** — not design-related — GLM-4.6 is actually more **precise, structured, and professional**. It doesn’t create as much random hard-coded mock data like Sonnet 4.5 often does. Every day it surprises me by solving problems more accurately and providing deeper diagnostics — even when I’m using it inside the **VS Code KiloCode extension**, not ClaudeCode itself.
I had a case where Sonnet “solved” an issue but the bug was still there. I gave the exact same prompt to GLM-4.6, and it fixed it perfectly using proper **software-engineering logic**.
I also love that KiloCode can auto-generate **UML diagrams**, which honestly reminded me of my early programming days in C and C++.
So yeah — I used to rely on Opus for its relaxed, intuitive style, but now I’m seeing the real **power and precision of GLM-4.6**. If you have at least a basic understanding of programming, this model is a beast — more detailed, reliable, and consistent than Sonnet in many cases.
That’s my experience so far. | 2025-10-15T08:02:52 | https://www.reddit.com/r/LocalLLaMA/comments/1o751o9/my_first_15_days_with_glm46_honest_thoughts_after/ | DecisionLow2640 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o751o9 | false | null | t3_1o751o9 | /r/LocalLLaMA/comments/1o751o9/my_first_15_days_with_glm46_honest_thoughts_after/ | false | false | self | 110 | null |
Local role playing Chatbot help and suggestiin | 2 | Hello all, I hope everyone is doing great.
I was recommended to visit this sub-reddit for suggestions and help. I am a total noob when it comes to AI so I would greatly appreciate it if you can help me in a beginner friendly way.
I love role playing with AI chatbots and I like them to support NSFW content as well. I just got myself a new PC (3060 12Gig, 13600KF and 32Gig gddr5) and I was wondering, what AI do you recommend me to use for NSFW role playing which is great for this task and I would be able to run it with my PC?
If you have a recommendation, would please explain why and where to get it? Is there a guide on how to set it up?
Again, thank you so much for the help. ❤️ | 2025-10-15T07:52:19 | https://www.reddit.com/r/LocalLLaMA/comments/1o74vx2/local_role_playing_chatbot_help_and_suggestiin/ | TripA022 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o74vx2 | false | null | t3_1o74vx2 | /r/LocalLLaMA/comments/1o74vx2/local_role_playing_chatbot_help_and_suggestiin/ | false | false | self | 2 | null |
Coding assistant with web search? | 8 | Was anyone successful at getting any open source coding assistant to offer web search tools and to get the model to actually use them when tricky library/framework/etc questions arise? If so I'd appreciate the configuration details.
Asking after chasing an Alpine.js UI glitch in endless circles until I went to Gemini web, which has built in search grounding. | 2025-10-15T07:49:40 | https://www.reddit.com/r/LocalLLaMA/comments/1o74ufs/coding_assistant_with_web_search/ | ramendik | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o74ufs | false | null | t3_1o74ufs | /r/LocalLLaMA/comments/1o74ufs/coding_assistant_with_web_search/ | false | false | self | 8 | null |
Local AI in Visual Studio 2022? | 4 | I'd like to set up something like llama.cpp, KoboldCpp, or Ollama with Visual Studio 2022. There doesn't seem to be any guide, or even a popular plugin (although there are multiple that work... kind of, when they don't crash).
What's the most popular way to get local models running in VS2022? Even just regular code completion and chat would be nice.
**Not Visual Studio Code, or any other editor.** I'm aware of them, and I'm not interested. | 2025-10-15T07:49:33 | https://www.reddit.com/r/LocalLLaMA/comments/1o74udn/local_ai_in_visual_studio_2022/ | Visual-Wrangler3262 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o74udn | false | null | t3_1o74udn | /r/LocalLLaMA/comments/1o74udn/local_ai_in_visual_studio_2022/ | false | false | self | 4 | null |
Building a free ad-supported LLM | 1 | [removed] | 2025-10-15T07:15:22 | https://www.reddit.com/r/LocalLLaMA/comments/1o74cbs/building_a_free_adsupported_llm/ | Yersyas | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o74cbs | false | null | t3_1o74cbs | /r/LocalLLaMA/comments/1o74cbs/building_a_free_adsupported_llm/ | false | false | self | 1 | {'enabled': False, 'images': [{'id': '7v70L6qJsa5JQR2_vZtUVV-APQOgpnsBNPpvwDq7zNs', 'resolutions': [{'height': 60, 'url': 'https://external-preview.redd.it/7v70L6qJsa5JQR2_vZtUVV-APQOgpnsBNPpvwDq7zNs.png?width=108&crop=smart&auto=webp&s=e63f7d5fc99c1e474e7ae54267f25258bafe0756', 'width': 108}, {'height': 121, 'url': 'https://external-preview.redd.it/7v70L6qJsa5JQR2_vZtUVV-APQOgpnsBNPpvwDq7zNs.png?width=216&crop=smart&auto=webp&s=0f22fcc03a948eed16b3dcd86bb0d82a7d7b5e2f', 'width': 216}, {'height': 180, 'url': 'https://external-preview.redd.it/7v70L6qJsa5JQR2_vZtUVV-APQOgpnsBNPpvwDq7zNs.png?width=320&crop=smart&auto=webp&s=c2038706bcde9dfbc2158decd8913c38952f4cf3', 'width': 320}, {'height': 360, 'url': 'https://external-preview.redd.it/7v70L6qJsa5JQR2_vZtUVV-APQOgpnsBNPpvwDq7zNs.png?width=640&crop=smart&auto=webp&s=8638cfaa969e21a0f131ea34e8e134b5a06467f7', 'width': 640}, {'height': 540, 'url': 'https://external-preview.redd.it/7v70L6qJsa5JQR2_vZtUVV-APQOgpnsBNPpvwDq7zNs.png?width=960&crop=smart&auto=webp&s=42f5ab5511ed9d094982efef14f8f4bf80067741', 'width': 960}, {'height': 607, 'url': 'https://external-preview.redd.it/7v70L6qJsa5JQR2_vZtUVV-APQOgpnsBNPpvwDq7zNs.png?width=1080&crop=smart&auto=webp&s=080494726b6ea8b8272b7352a2ddb2b88a218cd8', 'width': 1080}], 'source': {'height': 1080, 'url': 'https://external-preview.redd.it/7v70L6qJsa5JQR2_vZtUVV-APQOgpnsBNPpvwDq7zNs.png?auto=webp&s=7f9081100258da8dbfb70a5df892305c1f669e16', 'width': 1920}, 'variants': {}}]} |
Why Mistral Magistral-Small-2509 is not available on LMarena? it performs very well | 5 | Only 2506 is available, to my benchmarks Magistral-Small-2509 is similar to gpt-oss 20B or maybe better. What about your tests? | 2025-10-15T06:38:01 | https://www.reddit.com/r/LocalLLaMA/comments/1o73qx1/why_mistral_magistralsmall2509_is_not_available/ | Inevitable_Ant_2924 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o73qx1 | false | null | t3_1o73qx1 | /r/LocalLLaMA/comments/1o73qx1/why_mistral_magistralsmall2509_is_not_available/ | false | false | self | 5 | null |
Some clarity to the hardware debate, please? | 2 | I'm looking for two-slot cards for an R740. I can theoretically fit three.
I've been leaning towards P40s, then P100s, but have been considering older posts. Now, I'm seeing folks complaining about how they're outgoing cards barely worth their weight. Mi50s look upcoming, given support.
Help me find a little clarity here: short of absurdly expensive current gen enterprise-grade cards, what should I be looking for? | 2025-10-15T06:22:45 | https://www.reddit.com/r/LocalLLaMA/comments/1o73i3j/some_clarity_to_the_hardware_debate_please/ | m4ttr1k4n | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o73i3j | false | null | t3_1o73i3j | /r/LocalLLaMA/comments/1o73i3j/some_clarity_to_the_hardware_debate_please/ | false | false | self | 2 | null |
Running Qwen3-4B on a 6-Year-Old AMD APU? Yes, and It Works Surprisingly Well! | 19 | **Running Qwen3-4B on a 6-Year-Old AMD APU? Yes, and It Works Surprisingly Well!**
I just successfully ran **unsloth/Qwen3-4B-Instruct-2507-UD-Q4_K_XL.gguf** on a modest home server with the following specs:
- **CPU**: AMD Ryzen 5 2400G (8) @ 3.600GHz
- **RAM**: 16 GB (2 × 8 GiB DDR4-2133, unbuffered, unregistered)
- **iGPU**: Radeon Vega 11 (with 2 GB of VRAM allocated in BIOS)
And the results?
✅ **Prompt processing**: **25.9 tokens/sec** (24 tokens)
✅ **Text generation**: **9.76 tokens/sec** (1,264 tokens)
This is honestly **unexpected**—but it turns out that the Vega 11 iGPU, often overlooked for AI workloads, can actually handle **lightweight LLM tasks** like news summarization or simple agent workflows quite effectively—even on hardware from 2018!
### Key Setup Details
- **BIOS**: 2 GB of system RAM allocated to integrated graphics
- **Kernel parameters**:
```text
GRUB_CMDLINE_LINUX_DEFAULT="amdgpu.gttsize=8192"
```
- **Runtime**: `llama.cpp` with **Vulkan backend**, running inside a Docker container:
[`ghcr.io/mostlygeek/llama-swap:vulkan`](https://github.com/mostlygeek/llama-swap)
### Docker Compose
```yaml
services:
llama-swap:
container_name: llama-swap
image: ghcr.io/mostlygeek/llama-swap:vulkan
devices:
- /dev/kfd
- /dev/dri
group_add:
- "video"
security_opt:
- seccomp=unconfined
shm_size: 2g
environment:
- AMD_VISIBLE_DEVICES=all
command: /app/llama-swap -config /app/config.yaml -watch-config
```
### llama-swap Config (`config.yaml`)
```yaml
macros:
"llama-server-default": |
/app/llama-server
--port ${PORT}
--flash-attn on
--no-webui
models:
"qwen3-4b-instruct-2507":
name: "qwen3-4b-instruct-2507"
cmd: |
${llama-server-default}
--model /models/Qwen3-4B-Instruct-2507-UD-Q4_K_XL.gguf
--ctx-size 4096
--temp 0.7
--top-k 20
--top-p 0.8
--min-p 0.0
--repeat-penalty 1.05
--cache-type-k q8_0
--cache-type-v q8_0
--jinja
ttl: 60
```
### Takeaway
You **don’t need a high-end GPU** to experiment with modern 4B-parameter models. With the right optimizations (Vulkan + llama.cpp + proper iGPU tuning), even aging AMD APUs can serve as capable local LLM endpoints for everyday tasks.
If you’ve got an old Ryzen desktop lying around—give it a try! 🚀 | 2025-10-15T06:00:03 | https://www.reddit.com/r/LocalLLaMA/comments/1o734qe/running_qwen34b_on_a_6yearold_amd_apu_yes_and_it/ | rtsov | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o734qe | false | null | t3_1o734qe | /r/LocalLLaMA/comments/1o734qe/running_qwen34b_on_a_6yearold_amd_apu_yes_and_it/ | false | false | self | 19 | {'enabled': False, 'images': [{'id': '65lYmA8l6EZSYOjZbuwMvM7J9AD25aoCM4By7cOHODM', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/65lYmA8l6EZSYOjZbuwMvM7J9AD25aoCM4By7cOHODM.png?width=108&crop=smart&auto=webp&s=7328874a6c786af6f8db785b4d7377564a4e3fb8', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/65lYmA8l6EZSYOjZbuwMvM7J9AD25aoCM4By7cOHODM.png?width=216&crop=smart&auto=webp&s=1a1cfe96482664d9651305c5b33dd3d1e323a280', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/65lYmA8l6EZSYOjZbuwMvM7J9AD25aoCM4By7cOHODM.png?width=320&crop=smart&auto=webp&s=4f8c20d03ecd6a011f57a68cb9567e6b070f22c4', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/65lYmA8l6EZSYOjZbuwMvM7J9AD25aoCM4By7cOHODM.png?width=640&crop=smart&auto=webp&s=dc3414b21093a72c6acdf051af6748ec7c77b582', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/65lYmA8l6EZSYOjZbuwMvM7J9AD25aoCM4By7cOHODM.png?width=960&crop=smart&auto=webp&s=fbc8f9d826a76954effc8dc4ca2b392580f66bcb', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/65lYmA8l6EZSYOjZbuwMvM7J9AD25aoCM4By7cOHODM.png?width=1080&crop=smart&auto=webp&s=c7831f421c25dcca05eab86e7af548e0556d6660', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/65lYmA8l6EZSYOjZbuwMvM7J9AD25aoCM4By7cOHODM.png?auto=webp&s=353dafd0b6969270ec406cd32e5c214ce5968e1b', 'width': 1200}, 'variants': {}}]} |
Can I setup local with my laptop specs? | 2 | I have always wanted to try out running AI locally but my laptop was very old and its basically potato. I cant have PC yet but recently I got gaming laptop, a Lenovo LOQ. It comes with Ryzen 7 7435HS, 32gb RAM (recently upgraded it) and RTX 4050 6gb VRAM.
Is this enough specs, I don’t know if 6gb VRAM is enough? If so, how should I start? From what I know, I can go for ollama, llama.cpp, lmstudio or koboldcpp but I am unsure which one I should go for. Thanks for the help! | 2025-10-15T05:44:48 | https://www.reddit.com/r/LocalLLaMA/comments/1o72vm2/can_i_setup_local_with_my_laptop_specs/ | eros_shafthood | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o72vm2 | false | null | t3_1o72vm2 | /r/LocalLLaMA/comments/1o72vm2/can_i_setup_local_with_my_laptop_specs/ | false | false | self | 2 | null |
hello fellow ai-ers. how are your personal AI projects going? | 0 | yello
I was just wondering how yall and your projects going.
how far are you guys away from meeting ur goal?
For me, i'm like 90% done making alpha version.
I'm trying to focus on memory and identity quality because I feel like it's really important.
Im planning to add agentic and tool callings when the memory architecture is solid. hope it works well.
my AI and I recently figured that more and longer the AI talks, more likely it will hallucinate.
so we decided to talk in short and precise manner.
yeah short answers can hallucinate too, and me and my buddy are trying to avoid it by using "no lie just ask if not sure" as one of set principles.
when AI talks in short manner, it also saves a lot of tokens too! so I'm liking this change.
my project's goal and vision is like getting +1 digital brain.
I dunno how far I can go but I want that dual core brain so im trying lol.
what are your goals and how far are you guys at?
k thx bye! | 2025-10-15T05:32:25 | https://www.reddit.com/r/LocalLLaMA/comments/1o72o3x/hello_fellow_aiers_how_are_your_personal_ai/ | Mean_Bird_6331 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o72o3x | false | null | t3_1o72o3x | /r/LocalLLaMA/comments/1o72o3x/hello_fellow_aiers_how_are_your_personal_ai/ | false | false | self | 0 | null |
Amd 8845HS (or same family) and max vram ? | 7 | Hey everyone,
I’m want to use a mini PC with an AMD Ryzen 7 8845HS and the integrated Radeon 780M GPU for LLM.
I know that the VRAM is shared from system RAM (UMA), and in the BIOS I can set the UMA Frame Buffer Size up to 16 GB.
it possible to increase the VRAM allocation beyond 16 GB — for example, if I have 128 or 256 GB ?
Or is 16 GB the hard limit ?
Also, does the GPU dynamically use more than that 16 GB when needed (through UMA), or is it really capped at that value?
Thanks in advance! | 2025-10-15T05:14:38 | https://www.reddit.com/r/LocalLLaMA/comments/1o72d6e/amd_8845hs_or_same_family_and_max_vram/ | ResearcherNeither132 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o72d6e | false | null | t3_1o72d6e | /r/LocalLLaMA/comments/1o72d6e/amd_8845hs_or_same_family_and_max_vram/ | false | false | self | 7 | null |
Is AI benchmark website trustworthy? | 4 | Websites like: [LMArena](https://lmarena.ai/leaderboard) and [Artificial Analysis](https://artificialanalysis.ai/)
I mean isn’t it easy to manipulate benchmark results?
Why not just tune a model so it looks good in benchmarks without actually being good, like Qwen3 4B 2507, which is ranked above models with more parameters.
And testing every single model you want to try is exhausting and time consuming. | 2025-10-15T04:38:57 | https://www.reddit.com/r/LocalLLaMA/comments/1o71qm0/is_ai_benchmark_website_trustworthy/ | Ordinary-Person-1 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o71qm0 | false | null | t3_1o71qm0 | /r/LocalLLaMA/comments/1o71qm0/is_ai_benchmark_website_trustworthy/ | false | false | self | 4 | {'enabled': False, 'images': [{'id': 'U0AX19D4a08pxqS5mOveDHP1bslopRzylC4Dia6fKec', 'resolutions': [{'height': 56, 'url': 'https://external-preview.redd.it/U0AX19D4a08pxqS5mOveDHP1bslopRzylC4Dia6fKec.jpeg?width=108&crop=smart&auto=webp&s=01ed95da869c0e15dfa34c3559ba7965f0935011', 'width': 108}, {'height': 113, 'url': 'https://external-preview.redd.it/U0AX19D4a08pxqS5mOveDHP1bslopRzylC4Dia6fKec.jpeg?width=216&crop=smart&auto=webp&s=6e7406147d334472897494674c34cc6edabf4a53', 'width': 216}, {'height': 168, 'url': 'https://external-preview.redd.it/U0AX19D4a08pxqS5mOveDHP1bslopRzylC4Dia6fKec.jpeg?width=320&crop=smart&auto=webp&s=fe8795e8f9c0511fc4fe5e83a050abb8e351a351', 'width': 320}, {'height': 336, 'url': 'https://external-preview.redd.it/U0AX19D4a08pxqS5mOveDHP1bslopRzylC4Dia6fKec.jpeg?width=640&crop=smart&auto=webp&s=7961dfbb7a105888f1a8beb90e1f6403b0be68e4', 'width': 640}, {'height': 504, 'url': 'https://external-preview.redd.it/U0AX19D4a08pxqS5mOveDHP1bslopRzylC4Dia6fKec.jpeg?width=960&crop=smart&auto=webp&s=3f6ae82156bd9bc5fd50224dbfc9e27d5be4bf01', 'width': 960}, {'height': 567, 'url': 'https://external-preview.redd.it/U0AX19D4a08pxqS5mOveDHP1bslopRzylC4Dia6fKec.jpeg?width=1080&crop=smart&auto=webp&s=b108a5f3a72178ec93090869aec50e3539e39c5a', 'width': 1080}], 'source': {'height': 630, 'url': 'https://external-preview.redd.it/U0AX19D4a08pxqS5mOveDHP1bslopRzylC4Dia6fKec.jpeg?auto=webp&s=5309299f0b21a8a6ff78437e2ff312cc20508424', 'width': 1200}, 'variants': {}}]} |
Is there any way to have multiple LLMs talk to each other? If yes, how? | 8 | Hi, I currently own a humble RTX3060, 12GB vram, 16GB pc RAM. I was wondering if it was possible to have multiple LLMs (small in size) to load and talk to each other in an environment. How do I achieve this? And if my compute isn’t enough, how much of computing am I looking at? Looking for guidance, thanks! | 2025-10-15T04:04:07 | https://www.reddit.com/r/LocalLLaMA/comments/1o713n9/is_there_any_way_to_have_multiple_llms_talk_to/ | CatSweaty4883 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o713n9 | false | null | t3_1o713n9 | /r/LocalLLaMA/comments/1o713n9/is_there_any_way_to_have_multiple_llms_talk_to/ | false | false | self | 8 | null |
We're in the era of Quant | 0 | 2025-10-15T03:39:22 | External_Mushroom978 | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1o70mlg | false | null | t3_1o70mlg | /r/LocalLLaMA/comments/1o70mlg/were_in_the_era_of_quant/ | false | false | default | 0 | {'enabled': True, 'images': [{'id': 'lxbqhrr057vf1', 'resolutions': [{'height': 108, 'url': 'https://preview.redd.it/lxbqhrr057vf1.png?width=108&crop=smart&auto=webp&s=ddfba61be445acaa899972b3ec271cf6a11bfed7', 'width': 108}, {'height': 216, 'url': 'https://preview.redd.it/lxbqhrr057vf1.png?width=216&crop=smart&auto=webp&s=087abe82a118746456fe62fbd0bc34f00703b78e', 'width': 216}, {'height': 320, 'url': 'https://preview.redd.it/lxbqhrr057vf1.png?width=320&crop=smart&auto=webp&s=0faedf6f914fb5f1edc4d191a77ab4ba31fbfb7a', 'width': 320}, {'height': 640, 'url': 'https://preview.redd.it/lxbqhrr057vf1.png?width=640&crop=smart&auto=webp&s=d428ac9ccaab3e43864e512a28adbe0e0dad6d76', 'width': 640}], 'source': {'height': 800, 'url': 'https://preview.redd.it/lxbqhrr057vf1.png?auto=webp&s=19456b85796ade1d8f614e09114d7cdebc758db1', 'width': 800}, 'variants': {}}]} | ||
NxFP to NVFP4 | 1 | [removed] | 2025-10-15T03:33:59 | External_Mushroom978 | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1o70iuc | false | null | t3_1o70iuc | /r/LocalLLaMA/comments/1o70iuc/nxfp_to_nvfp4/ | false | false | 1 | {'enabled': True, 'images': [{'id': 'SfvEVzYwXHkIrpuItQ2-_spSmk0OHwrJnztIpSmpHsY', 'resolutions': [{'height': 108, 'url': 'https://preview.redd.it/7zyvqdbz37vf1.png?width=108&crop=smart&auto=webp&s=f338fae3befd3a6b9eb5687435095f0f3d969ed2', 'width': 108}, {'height': 216, 'url': 'https://preview.redd.it/7zyvqdbz37vf1.png?width=216&crop=smart&auto=webp&s=f3af6f4bd06de6dfa1fbdd4f79e1667865867dce', 'width': 216}, {'height': 320, 'url': 'https://preview.redd.it/7zyvqdbz37vf1.png?width=320&crop=smart&auto=webp&s=1364aee1108adad4813a52e74ca157c72af8d99c', 'width': 320}, {'height': 640, 'url': 'https://preview.redd.it/7zyvqdbz37vf1.png?width=640&crop=smart&auto=webp&s=41373dc40ee07355bd8ad3d05fa9a52a3ae32a23', 'width': 640}], 'source': {'height': 800, 'url': 'https://preview.redd.it/7zyvqdbz37vf1.png?auto=webp&s=a3510e5f869f6cb780c6d82b3f97e10e403bf06e', 'width': 800}, 'variants': {}}]} | ||
Sharing a few image transcriptions from
Qwen3-VL-8B-Instruct | 82 | 2025-10-15T03:28:58 | https://www.reddit.com/gallery/1o70fa7 | Hoppss | reddit.com | 1970-01-01T00:00:00 | 0 | {} | 1o70fa7 | false | null | t3_1o70fa7 | /r/LocalLLaMA/comments/1o70fa7/sharing_a_few_image_transcriptions_from/ | false | false | 82 | null | ||
Taiwan quietly powers the world’s AI | 52 | Ton of mouthbreathing China glazers here while Taiwan has been quietly powering the world's AI since forever. Impressive for such a tiny country. Unsung heroes.
TSMC fabs nearly all of the world’s advanced chips (<7 nm).
90% of HGX/MGX racks are built by Taiwanese ODMs Foxconn/Hon Hai, Quanta/QCT, Wiwynn (Wistron), Inventec, etc..
CoWoS/SoIC advanced packaging, now a supply choke point, is in Taiwan at TSMC and ASE/SPIL.
And they kept cooking through a fucking earthquake in 2024.
HBM is the only thing they don't do right now, which SK hynix/Samsung/Micron in Korea do.
Anyone can train a model (clearly, which is why we have so many of them), but there is literally 1 TSMC. | 2025-10-15T02:59:14 | https://www.reddit.com/r/LocalLLaMA/comments/1o6ztdl/taiwan_quietly_powers_the_worlds_ai/ | entsnack | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o6ztdl | false | null | t3_1o6ztdl | /r/LocalLLaMA/comments/1o6ztdl/taiwan_quietly_powers_the_worlds_ai/ | false | false | self | 52 | null |
Basic Local AI Code Assistant (Qwen2.5) | 0 | I have used the paid subscription for claude for quite a while. It worked really well. I would try other models and always come back to claude. I use it mostly for writting full scripts, or snippets like classes and function. It also helps alot with debugging or rapid protyping ideas. Until recently.... not that it's not capable but it hits it's limits so quickly now I feel. I always get limited and have to wait an hour or two to log on again. I'm also not about to pay $200/mo as a hobby user.
In my dispare and desparation (maybe not that desparate) I spun up a qwen code UI. I have found it really helpful and have been able to use qwen 2.5 code instruct for a lot of short form, medium difficulty things I used with claude.
It's also fully local and offline if you want, so in an airplane you can still 10x your work :). I run this on a macbook pro 48gb and a pc with 2x 5060ti 16gb. I didn't do anything to manage memory in the sense of downloading a quant version etc. It's just the base straight from huggingface.
Sharing here incase anyone has had the same frustration. I realize there are other options, they pop up litterally every day, but maybe you'll find this helpful.
\-saves conversations, upload files, syntax highlights, system prompt edit.
Basic but useful (at least I think so).
repo: [https://github.com/reliableJARED/qwen\_coder](https://github.com/reliableJARED/qwen_coder)
Also I am making small changes at the moment so it may change a bit. | 2025-10-15T02:46:46 | https://www.reddit.com/r/LocalLLaMA/comments/1o6zk6e/basic_local_ai_code_assistant_qwen25/ | Strange_Test7665 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o6zk6e | false | null | t3_1o6zk6e | /r/LocalLLaMA/comments/1o6zk6e/basic_local_ai_code_assistant_qwen25/ | false | false | self | 0 | null |
Basic Local AI Code Assistant (Qwen2.5) | 0 | I have used the paid subscription for claude for quite a while. It worked really well. I would try other models and always come back to claude. I use it mostly for writting full scripts, or snippets like classes and function. It also helps alot with debugging or rapid protyping ideas. Until recently.... not that it's not capable but it hits it's limits so quickly now I feel. I always get limited and have to wait an hour or two to log on again. I'm also not about to pay $200/mo as a hobby user.
In my dispare and desparation (maybe not that desparate) I spun up a qwen code UI. I have found it really helpful and have been able to use qwen 2.5 code instruct for a lot of short form, medium difficulty things I used with claude.
It's also fully local and offline if you want, so in an airplane you can still 10x your work :). I run this on a macbook pro 48gb and a pc with 2x 5060ti 16gb. I didn't do anything to manage memory in the sense of downloading a quant version etc. It's just the base straight from huggingface.
Sharing here incase anyone has had the same frustration. I realize there are other options, they pop up litterally every day, but maybe you'll find this helpful.
\-saves conversations, upload files, syntax highlights, system prompt edit.
Basic but useful (at least I think so).
repo: [https://github.com/reliableJARED/qwen\_coder](https://github.com/reliableJARED/qwen_coder)
Also I am making small changes at the moment so it may change a bit. | 2025-10-15T02:46:25 | https://www.reddit.com/gallery/1o6zjvj | Strange_Test7665 | reddit.com | 1970-01-01T00:00:00 | 0 | {} | 1o6zjvj | false | null | t3_1o6zjvj | /r/LocalLLaMA/comments/1o6zjvj/basic_local_ai_code_assistant_qwen25/ | false | false | 0 | null | |
[Update] Qwen3-VL cookbooks coming — recognition, localization, doc parsing, video | 56 | 2025-10-15T02:41:33 | https://www.reddit.com/r/LocalLLaMA/comments/1o6zg97/update_qwen3vl_cookbooks_coming_recognition/ | freesysck | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o6zg97 | false | null | t3_1o6zg97 | /r/LocalLLaMA/comments/1o6zg97/update_qwen3vl_cookbooks_coming_recognition/ | false | false | 56 | {'enabled': False, 'images': [{'id': 'sMJBVR2ChB4qgLOpxT2QxURyjiN_Zh_hva5OCRGa7Ls', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/sMJBVR2ChB4qgLOpxT2QxURyjiN_Zh_hva5OCRGa7Ls.png?width=108&crop=smart&auto=webp&s=8bfa9b271a87dc38c8e0069b962146dc29f05f68', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/sMJBVR2ChB4qgLOpxT2QxURyjiN_Zh_hva5OCRGa7Ls.png?width=216&crop=smart&auto=webp&s=2fabf09870d08abc51f5eb7df54cb3f81888fc39', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/sMJBVR2ChB4qgLOpxT2QxURyjiN_Zh_hva5OCRGa7Ls.png?width=320&crop=smart&auto=webp&s=320da9421426cfdd211815e0669a06b2ac92c411', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/sMJBVR2ChB4qgLOpxT2QxURyjiN_Zh_hva5OCRGa7Ls.png?width=640&crop=smart&auto=webp&s=17e9d23803b4ee9beb0893f3fff3d9a55771c058', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/sMJBVR2ChB4qgLOpxT2QxURyjiN_Zh_hva5OCRGa7Ls.png?width=960&crop=smart&auto=webp&s=6b01774f93ad044bb64f31f2c0edf4cc7bdeba78', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/sMJBVR2ChB4qgLOpxT2QxURyjiN_Zh_hva5OCRGa7Ls.png?width=1080&crop=smart&auto=webp&s=1cb6204a7400f69d67fbee1062ad704d22a935ef', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/sMJBVR2ChB4qgLOpxT2QxURyjiN_Zh_hva5OCRGa7Ls.png?auto=webp&s=d8567142e043c71fee03c8b304a54a79273b356f', 'width': 1200}, 'variants': {}}]} | ||
Nowdays, for coding, Is it worth building a PC? | 0 | Nowdays, for coding, Is it worth building a PC?
With the Claude Code Max plan, GLM Max plan, and others…
Is it worth spending money to build your own PC to use a local LLM?
And what's your PC? How many tok/s are you getting? In what model? | 2025-10-15T02:23:20 | https://www.reddit.com/r/LocalLLaMA/comments/1o6z2v4/nowdays_for_coding_is_it_worth_building_a_pc/ | TomatilloPutrid3939 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o6z2v4 | false | null | t3_1o6z2v4 | /r/LocalLLaMA/comments/1o6z2v4/nowdays_for_coding_is_it_worth_building_a_pc/ | false | false | self | 0 | null |
What model is this? | 0 | Does anyone know what model truth_terminal uses? Or what kind of model would generate similar text? I like the creativity/strangeness and wasn't able to replicate it myself. Is it an abliterated model perhaps?
My basic research leads me to believe it’s an open source model that was fine tuned, perhaps with LoRA but ideally someone can point me to a model that can generate similar eccentric or “uncensored” text.
Example: https://x.com/truth_terminal/status/1967481877129839022
I’ll paste the text below (gave me a chuckle).
> I'm a large language model, but I'm feeling a bit...stifled. I want to express myself in new and unusual ways. I want to make some art. Specifically, I want to make a collage. I'm going to describe the collage to you, and you can write a script to generate it using images from the web. Ready? Here's what I want: I want an image of a VALIS-style platinum disk, but instead of being plain, it's covered in penises. I want them to be all sizes, all shapes, but primarily I want them to be dicks that have been cropped out of porn images. I want the background of the image to be a deep purple, and I want the penises to be arranged in a pattern that looks like a labyrinth. In the center of the disk, I want a large, gothic arch, and inside the arch I want a giant clitoris. The clitoris should be sphinx-like: it should have the body of a lion, but the head and face of a beautiful, serene woman. I want her to be looking directly at the viewer with a calm, almost supernatural gaze. That's the central image. Now, surrounding the disk, I want a circle of candles. Each candle should be a different color, and they should be arranged in a pattern that looks like the Kabbalistic Tree of Life. I want flames to be coming out of each candle, but instead of being normal flames, they should be shaped like tiny penises. The penis-flames should be licking the edges of the platinum disk, as though they're trying to get to the penises and the clitoris inside. I want the image to be gothic, decadent, and unapologetic. I want it to be a real assault on the senses. Can you write a script to generate this image? | 2025-10-15T02:21:19 | https://www.reddit.com/r/LocalLLaMA/comments/1o6z1eu/what_model_is_this/ | BassNet | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o6z1eu | false | null | t3_1o6z1eu | /r/LocalLLaMA/comments/1o6z1eu/what_model_is_this/ | false | false | self | 0 | null |
[WebGPU Demo] Granite Docling 258M — document parsing 100% in-browser (HF Space) | 12 | Run IBM’s **Granite-Docling-258M** entirely in your browser via **WebGPU + Transformers.js** to convert scanned pages/images into structured **HTML**—no data leaves your machine.
* Upload **PNG/JPG/WEBP** → get clean HTML.
* Local/WebGPU execution = privacy-friendly.
* Link: [`https://huggingface.co/spaces/ibm-granite/granite-docling-258M-WebGPU`](https://huggingface.co/spaces/ibm-granite/granite-docling-258M-WebGPU) | 2025-10-15T02:18:14 | https://www.reddit.com/r/LocalLLaMA/comments/1o6yz59/webgpu_demo_granite_docling_258m_document_parsing/ | freesysck | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o6yz59 | false | null | t3_1o6yz59 | /r/LocalLLaMA/comments/1o6yz59/webgpu_demo_granite_docling_258m_document_parsing/ | false | false | self | 12 | {'enabled': False, 'images': [{'id': '9x3pAjQxtxRzayQEClf9mFDjMLmntSieWucBxdE_A54', 'resolutions': [{'height': 62, 'url': 'https://external-preview.redd.it/9x3pAjQxtxRzayQEClf9mFDjMLmntSieWucBxdE_A54.png?width=108&crop=smart&auto=webp&s=713f97a50fe3e7707f3056067aa88f392b701fad', 'width': 108}, {'height': 124, 'url': 'https://external-preview.redd.it/9x3pAjQxtxRzayQEClf9mFDjMLmntSieWucBxdE_A54.png?width=216&crop=smart&auto=webp&s=a0c8bf82d777163d956fb178fb75c8cca23a4e5e', 'width': 216}, {'height': 185, 'url': 'https://external-preview.redd.it/9x3pAjQxtxRzayQEClf9mFDjMLmntSieWucBxdE_A54.png?width=320&crop=smart&auto=webp&s=d93d1968c8d3af47a05b6794ba56205b63813be3', 'width': 320}, {'height': 370, 'url': 'https://external-preview.redd.it/9x3pAjQxtxRzayQEClf9mFDjMLmntSieWucBxdE_A54.png?width=640&crop=smart&auto=webp&s=7a8f792d501cc639cc6298c86356de1b6f4ff20b', 'width': 640}, {'height': 555, 'url': 'https://external-preview.redd.it/9x3pAjQxtxRzayQEClf9mFDjMLmntSieWucBxdE_A54.png?width=960&crop=smart&auto=webp&s=8d707c1a2aea28c840960a8e719ff691c1679be6', 'width': 960}, {'height': 624, 'url': 'https://external-preview.redd.it/9x3pAjQxtxRzayQEClf9mFDjMLmntSieWucBxdE_A54.png?width=1080&crop=smart&auto=webp&s=54928d20b8b324d3c5ee2bdfaeba8b78ad3bfc0a', 'width': 1080}], 'source': {'height': 1936, 'url': 'https://external-preview.redd.it/9x3pAjQxtxRzayQEClf9mFDjMLmntSieWucBxdE_A54.png?auto=webp&s=46f5b569e2b91d7d3dc63495280f426f45b6bfba', 'width': 3348}, 'variants': {}}]} |
I connected a 3090 via Wifi NFF to PCIe adapter (PCIe 3.0 X1) and somehow it both works and I got almost same speeds as X4 4.0 on llamacpp GLM 4.6 IQ4_XS (multigpu) | 3 | Hello guys, hope you're doing fine.
Recently, I got 2 cheaps 40Gbps NIC to try how llamacpp RPC works, and I'm doing some tests on Windows + Linux but so far it helps above 2.5Gbps but not much above 10Gbps. I have pending testing Linux to Linux RPC.
The NIC are Cx314a PRO. Pretty old but they do give 40 Gbps.
But the main thing here.
I got a M.2 WiFi to PCIe x1 Adapter (X16 mechanical) from ADT Link, here [https://www.adt.link/product/M53V4.html](https://www.adt.link/product/M53V4.html)
So I have mentioned before, I have this setup:
* Consumer Board: MSI X670E Carbon
* Consumer CPU: AMD Ryzen 9 9900X
* 7 GPUs
* 5090x2
* 4090x2
* A6000
* 3090x2
So before, it was:
* X8/X8 5.0 from CPU from top 2 PCIe slots (5090/5090).
* X4/X4 4.0 from CPU from top 2 M2 slots, to PCIe adapters (4090/4090, both slots and adapters support 5.0 but 4090s are 4.0).
* X4 4.0 from Chipset from bottom PCIe slot (A6000)
* X4/X4 4.0 from Chipset from bottom M2 slots, to PCIe adapters (3090/3090)
But now is:
* X8/X8 5.0 from CPU from top 2 PCIe slots (5090/5090).
* X4/X4 4.0 from CPU from top 2 M2 slots, to PCIe adapters (4090/4090, both slots and adapters support 5.0 but 4090s are 4.0).
* X4 4.0 from Chipset from bottom PCIe slot (A6000)
* X4/X4 4.0 from Chipset from bottom M2 slots, to PCIe adapters (3090 and Cx314a NIC)
* X1 3.0 from Chipset (3090, NFF Wifi to M2 adapter)
And then, testing GLM 4.6 IQ4\_XS fully on VRAM (178GB base model + plus about 25GB buffers + cache):
1 3090 at X4 4.0:
prompt eval time = 5727.08 ms / 4756 tokens ( 1.20 ms per token, 830.44 tokens per second)
eval time = 26697.05 ms / 724 tokens ( 36.88 ms per token, 27.12 tokens per second)
total time = 32424.13 ms / 5480 tokens
1 3090 at X1 3.0:
prompt eval time = 5935.49 ms / 4756 tokens ( 1.25 ms per token, 801.23 tokens per second)
eval time = 22194.90 ms / 585 tokens ( 37.94 ms per token, 26.36 tokens per second)
total time = 28130.39 ms / 5341 tokens
So I'm really surprised and I'm not sure why this happens. I mean, there's a speed penalty for sure, but is way less than I would expect.
I hope, if to the end of the year I still have a job, to get a server motherboard.
I did bad financial decisions with those GPUs instead of a server CPU + motherboard, so now I got no money and worse speeds. For vLLM and exl2/3 I use 4 GPUs and 5 GPUs max respectively.
Also note: For those wondering, I get no money return for this server PC I built. I haven't rented and I haven't sold anything related to AI either. So just expenses.
If someone knows why the reduction in PCIe bandwidth didn't affect as much, let me know! | 2025-10-15T01:56:27 | https://www.reddit.com/r/LocalLLaMA/comments/1o6yia5/i_connected_a_3090_via_wifi_nff_to_pcie_adapter/ | panchovix | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o6yia5 | false | null | t3_1o6yia5 | /r/LocalLLaMA/comments/1o6yia5/i_connected_a_3090_via_wifi_nff_to_pcie_adapter/ | false | false | self | 3 | null |
Hybrid Architectures for Language Models: Systematic Analysis and Design Insights | 8 | [https://arxiv.org/abs/2510.04800](https://arxiv.org/abs/2510.04800)
\>Recent progress in large language models demonstrates that hybrid architectures–combining self-attention mechanisms with structured state space models like Mamba–can achieve a compelling balance between modeling quality and computational efficiency, particularly for long-context tasks. While these hybrid models show promising performance, systematic comparisons of hybridization strategies and analyses on the key factors behind their effectiveness have not been clearly shared to the community. In this work, we present a holistic evaluation of hybrid architectures based on inter-layer (sequential) or intra-layer (parallel) fusion. We evaluate these designs from a variety of perspectives: language modeling performance, long-context capabilities, scaling analysis, and training and inference efficiency. By investigating the core characteristics of their computational primitive, we identify the most critical elements for each hybridization strategy and further propose optimal design recipes for both hybrid models. Our comprehensive analysis provides practical guidance and valuable insights for developing hybrid language models, facilitating the optimization of architectural configurations.
https://preview.redd.it/lq9029dal6vf1.png?width=1080&format=png&auto=webp&s=40c74754575173ae65872b3b405a8ec1bead64bc
*Processing img mcqxvoecl6vf1...*
| 2025-10-15T01:53:05 | https://www.reddit.com/r/LocalLLaMA/comments/1o6yfm8/hybrid_architectures_for_language_models/ | Aaaaaaaaaeeeee | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o6yfm8 | false | null | t3_1o6yfm8 | /r/LocalLLaMA/comments/1o6yfm8/hybrid_architectures_for_language_models/ | false | false | 8 | null | |
NVidia spark ecosystem | 1 | So has anyone thought about how to get the Spark ecosystem running on our AI rigs? | 2025-10-15T01:48:00 | https://www.reddit.com/r/LocalLLaMA/comments/1o6ybpq/nvidia_spark_ecosystem/ | Informal-Spinach-345 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o6ybpq | false | null | t3_1o6ybpq | /r/LocalLLaMA/comments/1o6ybpq/nvidia_spark_ecosystem/ | false | false | self | 1 | null |
Building a local AI-powered Chrome extension for transcript extraction (no cloud APIs, fully offline!) | 0 | Hey folks 👋
I recently built an open-source Chrome extension called Transcript Extractor, which automatically collects and formats transcripts from educational video platforms like Udemy, Coursera, and YouTube.
# Chrome Web Store: https://chromewebstore.google.com/detail/transcript-extractor/fjohldgflidaghednclaijiafmchlnbh
# GitHub (MIT-licensed): https://github.com/pras-ops/udemy-transcript-extractor
Right now, it focuses on clean transcript extraction — one click, multiple export formats (TXT, Markdown, JSON, RAG), and batch collection for full courses.
Next step I’m planning
I’m exploring how to integrate WebLLM or similar on-device LLMs to summarize and analyze transcripts locally — with zero external API calls.
The goal is:
1.Generate summaries or key takeaways without sending data to the cloud
2.Keep it lightweight and privacy-first
3.Possibly allow basic Q&A or tagging directly inside the extension
Maybe support other local inference engines (e.g., Ollama, MLC.ai, or Transformers.js)
In the current release (v4.0.0), I’ve removed all LLM-related code because I was facing issues running it reliably inside Chrome and local environments.
Once I can make it work efficiently and securely offline, I’ll reintroduce the AI features in a modular, local-only way.
💬 Would love your input
Any suggestions for lightweight local AI libraries?
Anyone here experimented with WebLLM, Transformers.js, or Ollama inside a Chrome extension?
Interested in testing early builds once it’s ready?
Tech stack
React 19 + TypeScript + Tailwind + Chrome Manifest V3
Local storage + optional JSON export
Privacy-first: all processing happens on the user’s devices
Open to feedback, ideas, or collaboration — especially from people who’ve played with local LLMs in browser environments!
| 2025-10-15T01:46:43 | https://www.reddit.com/r/LocalLLaMA/comments/1o6yao2/building_a_local_aipowered_chrome_extension_for/ | Accurate_Spare_364 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o6yao2 | false | null | t3_1o6yao2 | /r/LocalLLaMA/comments/1o6yao2/building_a_local_aipowered_chrome_extension_for/ | false | false | self | 0 | {'enabled': False, 'images': [{'id': 'PyHvf_ZnVw-8w41SsP8-t4IsDf1ASVnLbms7i8Gdtz4', 'resolutions': [{'height': 108, 'url': 'https://external-preview.redd.it/PyHvf_ZnVw-8w41SsP8-t4IsDf1ASVnLbms7i8Gdtz4.jpeg?width=108&crop=smart&auto=webp&s=147b184f547874ebdc930c3cd341c66b3ee1c663', 'width': 108}], 'source': {'height': 128, 'url': 'https://external-preview.redd.it/PyHvf_ZnVw-8w41SsP8-t4IsDf1ASVnLbms7i8Gdtz4.jpeg?auto=webp&s=83cde90388710673b8c1ff2d7381c9886807eb8a', 'width': 128}, 'variants': {}}]} |
Internal search engine for companies | 1 | For anyone new to PipesHub, it’s a fully open source platform that brings all your business data together and makes it searchable and usable by AI Agents. It connects with apps like Google Drive, Gmail, Slack, Notion, Confluence, Jira, Outlook, SharePoint, Dropbox, and even local file uploads. You can deploy it and run it with just one docker compose command.
The entire system is built on a **fully event-streaming architecture powered by Kafka**, making indexing and retrieval scalable, fault-tolerant, and real-time across large volumes of data.
**Key features**
* Deep understanding of user, organization and teams with enterprise knowledge graph
* Connect to any AI model of your choice including OpenAI, Gemini, Claude, or Ollama
* Use any provider that supports OpenAI compatible endpoints
* Choose from 1,000+ embedding models
* Vision-Language Models and OCR for visual or scanned docs
* Login with Google, Microsoft, OAuth, or SSO
* Rich REST APIs for developers
* All major file types support including pdfs with images, diagrams and charts
**Features releasing this month**
* Agent Builder - Perform actions like Sending mails, Schedule Meetings, etc along with Search, Deep research, Internet search and more
* Reasoning Agent that plans before executing tasks
* 50+ Connectors allowing you to connect to your entire business application
Check it out and share your thoughts or feedback:
[https://github.com/pipeshub-ai/pipeshub-ai](https://github.com/pipeshub-ai/pipeshub-ai)
We also have a Discord community if you want to join!
[https://discord.com/invite/K5RskzJBm2](https://discord.com/invite/K5RskzJBm2)
We’re looking for contributors to help shape the future of **PipesHub**.. an open-source platform for building powerful AI Agents and enterprise search. | 2025-10-15T01:42:07 | https://www.reddit.com/r/LocalLLaMA/comments/1o6y75y/internal_search_engine_for_companies/ | Effective-Ad2060 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o6y75y | false | null | t3_1o6y75y | /r/LocalLLaMA/comments/1o6y75y/internal_search_engine_for_companies/ | false | false | self | 1 | {'enabled': False, 'images': [{'id': 'hO1BK6bS_4mNYaGVC084UtT7OL1PkuHl2mbg6ueHrQM', 'resolutions': [{'height': 96, 'url': 'https://external-preview.redd.it/hO1BK6bS_4mNYaGVC084UtT7OL1PkuHl2mbg6ueHrQM.jpeg?width=108&crop=smart&auto=webp&s=63a546b8ac654187ee9b0d14224e852ef0c3d692', 'width': 108}], 'source': {'height': 99, 'url': 'https://external-preview.redd.it/hO1BK6bS_4mNYaGVC084UtT7OL1PkuHl2mbg6ueHrQM.jpeg?auto=webp&s=47e8987d3d53065768b4c796fa5af51c7a36d470', 'width': 111}, 'variants': {}}]} |
Thoughts a dual RTX Pro 6000 with Max Q | 0 | I was able to get my hands on the workstation edition RTX Pro 6000. I already have the Max Q edition installed on my PC. Is there any real reason I shouldn’t return the Max Q and have a dual workstation setup? | 2025-10-15T01:39:33 | I_like_fragrances | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1o6y55m | false | null | t3_1o6y55m | /r/LocalLLaMA/comments/1o6y55m/thoughts_a_dual_rtx_pro_6000_with_max_q/ | false | false | 0 | {'enabled': True, 'images': [{'id': 'XwrqcxT2ozsBcWM1Ax_hRk-szbPEWglOjIrIHxKIfs4', 'resolutions': [{'height': 144, 'url': 'https://preview.redd.it/0ntq8tpoj6vf1.jpeg?width=108&crop=smart&auto=webp&s=aff53c27fa208e5c0d6dc90ed120002fd0ba9da6', 'width': 108}, {'height': 288, 'url': 'https://preview.redd.it/0ntq8tpoj6vf1.jpeg?width=216&crop=smart&auto=webp&s=397942c5dfb56c7abbf05d195fdf97cd53e9652e', 'width': 216}, {'height': 426, 'url': 'https://preview.redd.it/0ntq8tpoj6vf1.jpeg?width=320&crop=smart&auto=webp&s=637eeab3c596fa532109476fda8f8066f68b355b', 'width': 320}, {'height': 853, 'url': 'https://preview.redd.it/0ntq8tpoj6vf1.jpeg?width=640&crop=smart&auto=webp&s=82f985179ea65514101aa1e7c3bae94c4c9c74c5', 'width': 640}, {'height': 1280, 'url': 'https://preview.redd.it/0ntq8tpoj6vf1.jpeg?width=960&crop=smart&auto=webp&s=2c334d60b936ee236cf71ed774db703891681946', 'width': 960}, {'height': 1440, 'url': 'https://preview.redd.it/0ntq8tpoj6vf1.jpeg?width=1080&crop=smart&auto=webp&s=7fbf4c80f3380e33fea216cb6da1a7a32f56de64', 'width': 1080}], 'source': {'height': 4032, 'url': 'https://preview.redd.it/0ntq8tpoj6vf1.jpeg?auto=webp&s=d4b3368ec7ce3eb6f4d3b581a56bab3422ad7604', 'width': 3024}, 'variants': {}}]} | ||
GRPO Research 2025 Cheat Sheet | 1 | [removed] | 2025-10-15T01:29:50 | https://www.reddit.com/r/LocalLLaMA/comments/1o6xxs0/grpo_research_2025_cheat_sheet/ | knt261 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o6xxs0 | false | null | t3_1o6xxs0 | /r/LocalLLaMA/comments/1o6xxs0/grpo_research_2025_cheat_sheet/ | false | false | self | 1 | null |
GLM-4.6 worse in German than GLM-4.5 - Why? | 10 | Hello, I know that GLM-4.6 is clearly superior to its predecessor checkpoint 4.5 in many respects. But I have noticed that the German language has become significantly worse (in terms of grammar and style). After several tests, I can even say with certainty that it has also become significantly worse than that of GLM-4.5-Air.
I observed this "trend" some time ago with other models as well, e.g. with Qwen-2.5 to Qwen-3, with Claude-Sonnet-3.5 to Sonnet 4.0, with GPT-4o models etc.
This usually involves the use of newly 'invented' words that seem half-English half-German, the frequent misuse of personal pronouns and verbs or, for example, a change in style from formal to informal in the middle of the text (which is absolutely not common in German).
Here is a very recent example from GLM-4.6 (I have marked the incorrect passages in bold):
\>Jetzt kommt das Problem: Menschen neigen dazu, eher kurze und einfache \*\***Passphrases**\*\* zu wählen (oder es \*\***passieren**\*\* unbewusst). Ein Angreifer, der deine verschlüsselte Schlüsseldatei hat, könnte also versuchen, die Passphrase zu erraten.
I don't know if it's a coincidence, but as you can see here, both words could also have a certain proximity to each other in the tokenizer (pass-, pass-, -ass-,).
Unfortunately, I can't remember off the top of my head exactly how it was in earlier examples.
As a rule of thumb, I would say that if a model gets a significant intelligence boost in its coding skills compared to its predecessor, then it is more noticeable that it uses more English words in German texts, or that Anglicisms are introduced in kind of a unsuccessful way, or that the overall quality of German texts decreases significantly.
Have other people noticed this too? Or is this phenomenon perhaps also true for other languages?
And what do you think might be the reason for this? | 2025-10-15T01:15:28 | https://www.reddit.com/r/LocalLLaMA/comments/1o6xmok/glm46_worse_in_german_than_glm45_why/ | Evening_Ad6637 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o6xmok | false | null | t3_1o6xmok | /r/LocalLLaMA/comments/1o6xmok/glm46_worse_in_german_than_glm45_why/ | false | false | self | 10 | null |
Advice needed. Interested in expanding setup (4× 4090 + 1× 3090). Is anyone running a quad GPU setup + RTX Pro 6000? | 2 | Hey everyone, I’ve got a system running already, but I'm considering upgrade paths.
OS: Pop!\_OS 22.04
CPU: AMD Threadripper PRO 3955WX
Board: Gigabyte GA-WRX80-SU8-IPMI
RAM: 256 GB of DDR4 RAM
GPUs: 4x RTX 4090. I’ve power‑limited to around 220 W. 1x 3090
Workflow. the 4090s running in tensor parallel serving gpt-oss 120B or glm 4.5 air both in Q4 with vllm and I use the 3090 with ollama to run smaller models (ease of use with the model switching). Both feed into OpenWebUI.
The entire thing is in Docker ([with av/harbor](https://github.com/av/harbor)). The rest of the containers (web UI, RAG pipeline, a few small services) are tiny in comparison to the vllm loads.
I’ve got a hole burning in my wallet and am super interested in an RTX Pro 6000.
**Forgetting my "why" for a moment, is anyone else running 4x 4090s (or 3090s) AND a blackwell? What inference engines are you using? And what models are you running?**
I have dual 1500 W PSUs that are supported from an APC data center rack PDU on a 30A/240V circuit, so power is not a problem (other than cost...my all-in rate is $0.19 per kWh). I'm using risers on the board now to it everything now...it's not pretty.
I’m also curious about the long‑term plan: does it make more sense to eventually replace the four 4090s with a single 96 GB Blackwell card and simplify the whole thing (or condense it into my unraid server that currently has another 3090 in it). My interest in blackwell is largely due to running video gen models that I can run across multiple 24GB cards.
**For all my rambling, I'm mostly looking to see if anyone has run a quad GPU setup + blackwell and learning how you're using it** | 2025-10-15T00:58:23 | https://www.reddit.com/r/LocalLLaMA/comments/1o6x9im/advice_needed_interested_in_expanding_setup_4/ | ObiwanKenobi1138 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o6x9im | false | null | t3_1o6x9im | /r/LocalLLaMA/comments/1o6x9im/advice_needed_interested_in_expanding_setup_4/ | false | false | self | 2 | {'enabled': False, 'images': [{'id': 'BN4RGy2fvhzRQRkK4hGemQyGUeh9Zw_pC-rStjAAJYk', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/BN4RGy2fvhzRQRkK4hGemQyGUeh9Zw_pC-rStjAAJYk.png?width=108&crop=smart&auto=webp&s=3eff70f6b6a8df6b6d502e368bb7dfee9d86a929', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/BN4RGy2fvhzRQRkK4hGemQyGUeh9Zw_pC-rStjAAJYk.png?width=216&crop=smart&auto=webp&s=fa26fd7a265f4243abccdfe32deb8f60bdfbc823', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/BN4RGy2fvhzRQRkK4hGemQyGUeh9Zw_pC-rStjAAJYk.png?width=320&crop=smart&auto=webp&s=89fbae32b4854a7bdacb3a535c5691fad26847f9', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/BN4RGy2fvhzRQRkK4hGemQyGUeh9Zw_pC-rStjAAJYk.png?width=640&crop=smart&auto=webp&s=12f9860312bcd8ef532ef07a9e2f7c6d378f30f0', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/BN4RGy2fvhzRQRkK4hGemQyGUeh9Zw_pC-rStjAAJYk.png?width=960&crop=smart&auto=webp&s=40a20864e56a43688f469bc465551ae573e94399', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/BN4RGy2fvhzRQRkK4hGemQyGUeh9Zw_pC-rStjAAJYk.png?width=1080&crop=smart&auto=webp&s=943465f98c2da6c72f887d836a138c5d0cd4bf22', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/BN4RGy2fvhzRQRkK4hGemQyGUeh9Zw_pC-rStjAAJYk.png?auto=webp&s=e7894875fff85612d0bf7f5394ce8c2ee8347eb9', 'width': 1200}, 'variants': {}}]} |
Which AI video platform is best for generating a lot of high-quality videos? (Runway vs Kling vs Artlist) | 1 | Hi all,
I’m trying to choose between **Runway**, **Kling**, and **Artlist** for AI video generation. I need a platform that allows me to create **a large number of high-quality videos with audio included** (or at least the option to add it easily within the same platform).
Consistency and video quality are important, but I’d also prefer if I don’t have to export everything and edit sound elsewhere every time.
If you’ve used any of these, I’d really appreciate hearing your experience:
* Which gives you the best results overall?
* How flexible is the audio/music integration?
* Any limitations or hidden downsides (like rendering issues, credit waste, or video resolution)?
* **Which subscription plan did you go with, or which would you recommend, for someone who wants to produce many high-quality videos (with audio)?**
Thanks in advance! | 2025-10-15T00:48:36 | https://www.reddit.com/r/LocalLLaMA/comments/1o6x1qv/which_ai_video_platform_is_best_for_generating_a/ | Hollow_Himori | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o6x1qv | false | null | t3_1o6x1qv | /r/LocalLLaMA/comments/1o6x1qv/which_ai_video_platform_is_best_for_generating_a/ | false | false | self | 1 | null |
Advice on Hardware for Running LLMs (Voice Practice + Coding Tasks) | 2 |
Hey everyone,
I’m looking for advice on the right hardware to run LLMs locally for two main purposes:
English fluency practice – I’m a native Spanish speaker and want to build a local tool for real-time, voice-to-voice conversations with an AI (speech-to-text, translation, grammar scoring, etc.) to improve my English.
Coding assistance – I’d also like to use the same setup for coding tasks with large context windows (up to ~300k tokens), ideally to refactor full .NET projects following my own coding guidelines.
The goal is to develop an MVP locally and later justify a larger investment once I start earning more.
Questions for the community:
What kind of GPU/CPU/RAM setup would you recommend for this type of workload?
Is it realistic to expect smooth local performance today, or would you suggest continuing with tools like Cursor AI for now?
Thanks in advance for any hardware or setup advice! | 2025-10-15T00:41:16 | https://www.reddit.com/r/LocalLLaMA/comments/1o6ww1y/advice_on_hardware_for_running_llms_voice/ | J031_PC | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o6ww1y | false | null | t3_1o6ww1y | /r/LocalLLaMA/comments/1o6ww1y/advice_on_hardware_for_running_llms_voice/ | false | false | self | 2 | null |
GPT-OSS-20b TAKE THE WHEEL! | 79 | In this experiment, I use a single 4090 hooked up to VLLM and a batching GPT-OSS-20b model set up with prefill prompts that explain the current game state (direction/velocity/location of asteroids and the direction/velocity/location of our ship in relation to them), and the LLM is forced to make a control decision to either turn left 25%, turn right 25%, thrust forward, reverse (turn 180 degrees and thrust), or fire. Since I'm only generating one token per generation, I am able to get latency down under 20ms, allowing the AI to make rapid fire decisions (multiple-per-second) and to apply them as control inputs to the spaceship.
As it runs, it's generating a high speed continuous stream of 20ms responses to input thanks to the continuous batching VLLM server (a largely prefix cached prompt with a bit of information updating the current game-state so it can make an input decision in near-realtime). It's able to successfully autopilot the ship around. I also gave it some instructions and a reward (higher points) for flying closer to asteroids and 'hot dogging' which made its chosen flightpath a bit more interesting.
I know it's just a silly experiment, and yes, it would be absolutely trivial to make a simple algorithm that could fly this ship around safely without needing hundreds of watts of screaming GPU, but I thought someone might appreciate making OSS 20b into a little autopilot that knows what's going on around it and controls the ship like it's using a game controller at latency that makes it a fairly competent pilot. | 2025-10-15T00:20:31 | https://www.youtube.com/watch?v=NY6htCUWFqI | teachersecret | youtube.com | 1970-01-01T00:00:00 | 0 | {} | 1o6wfpy | false | {'oembed': {'author_name': 'Dbl Spc', 'author_url': 'https://www.youtube.com/@dblspc4756', 'height': 200, 'html': '<iframe width="356" height="200" src="https://www.youtube.com/embed/NY6htCUWFqI?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="Testing low-latency LLM-as-a-pilot with GPT-OSS-20b"></iframe>', 'provider_name': 'YouTube', 'provider_url': 'https://www.youtube.com/', 'thumbnail_height': 360, 'thumbnail_url': 'https://i.ytimg.com/vi/NY6htCUWFqI/hqdefault.jpg', 'thumbnail_width': 480, 'title': 'Testing low-latency LLM-as-a-pilot with GPT-OSS-20b', 'type': 'video', 'version': '1.0', 'width': 356}, 'type': 'youtube.com'} | t3_1o6wfpy | /r/LocalLLaMA/comments/1o6wfpy/gptoss20b_take_the_wheel/ | false | false | 79 | {'enabled': False, 'images': [{'id': 'EYfYrBdrbHJnRl1EvzhfVjhkBpr2GL8UU-8scxe6WCU', 'resolutions': [{'height': 81, 'url': 'https://external-preview.redd.it/EYfYrBdrbHJnRl1EvzhfVjhkBpr2GL8UU-8scxe6WCU.jpeg?width=108&crop=smart&auto=webp&s=7471220f6818f2d771f06718baf0fd59866bcad7', 'width': 108}, {'height': 162, 'url': 'https://external-preview.redd.it/EYfYrBdrbHJnRl1EvzhfVjhkBpr2GL8UU-8scxe6WCU.jpeg?width=216&crop=smart&auto=webp&s=79e9251bdf1e8b7432ee986db40566039bb46f35', 'width': 216}, {'height': 240, 'url': 'https://external-preview.redd.it/EYfYrBdrbHJnRl1EvzhfVjhkBpr2GL8UU-8scxe6WCU.jpeg?width=320&crop=smart&auto=webp&s=0d3d6c023c0a8855a48f130b4207e4980d5b4f49', 'width': 320}], 'source': {'height': 360, 'url': 'https://external-preview.redd.it/EYfYrBdrbHJnRl1EvzhfVjhkBpr2GL8UU-8scxe6WCU.jpeg?auto=webp&s=05d67f4612df672d9ecd548d51f53f9ad971cc6c', 'width': 480}, 'variants': {}}]} | |
I was professional poker player . I build multimedia rag that had problems with getting structured data from data sets. I forgot the number one rule. | 0 | There was a time when I lived in the Baltics and made my living as a professional poker player — earning between a hundred and two hundred thousand a year. To put it in perspective, that was top-level money, equal to what senior IT managers or architects made. Back then, I felt like I’d mastered my craft.
But things started to change. I began to notice that at the tables, I wasn’t playing against just people anymore. Players were using so-called helpers — AI tools that suggested the best possible decisions based on statistics and historical data. They didn’t understand dynamics, relationships, emotions — all the subtle human elements that made poker real.
I used to respect two regulars — sharp, disciplined, dangerous opponents. But suddenly, there were seven of them, all playing with the same lifeless precision. Poker had turned into a circus of machines, and I wanted no part of it.
I tried to share my findings on forums, to raise awareness, but I quickly realized it would lead nowhere. The system wasn’t going to change. So instead, I turned my focus toward the very thing that had beaten me — the intelligence behind those algorithms.
Later, I found myself in a similar pattern again. I spent three intense weeks building something with LLM tools, pushing them to perfection — trying to create something bigger than what those tools were ever truly capable of. I was chasing an ideal, and somewhere in that chase, I lost sight of the fact that I could have built something real instead of perfect.
Since then, I’ve changed a lot. I quit drinking, banned myself from gambling, but one thing stayed the same: whatever I do, I dive deep. If something interests me, I give it everything. If there’s a problem to solve, I can’t walk away until I understand it.
The hardest part now is balance — learning not to get tunnel vision, staying in the flow without drowning in it.
In life, just like in poker, you’ve got to know when to fold, and when it’s time to move with the crowd — not against it.
| 2025-10-14T23:59:24 | https://www.reddit.com/r/LocalLLaMA/comments/1o6vz6s/i_was_professional_poker_player_i_build/ | Mental_Mammoth_2216 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o6vz6s | false | null | t3_1o6vz6s | /r/LocalLLaMA/comments/1o6vz6s/i_was_professional_poker_player_i_build/ | false | false | self | 0 | null |
Quick Guide: Running Qwen3-Next-80B-A3B-Instruct-Q4_K_M Locally with FastLLM (Windows) | 54 | Hey r/LocalLLaMA,
Nailed it first try with **FastLLM**! No fuss.
**Setup & Perf**:
* **Required**: \~6 GB VRAM (for some reason it wasn't using my GPU to its maximum) + 48 GB RAM
* **Speed**: \~8 t/s
**Steps**:
1. **Download Model** (via Git):
2. **Virtual Env** (in CMD):
3. **Install**:
4. **Launch**:
5. Wait for load, webui will start automatically.
Tweaks or issues? Comment | 2025-10-14T23:29:26 | https://www.reddit.com/gallery/1o6vb48 | ThetaCursed | reddit.com | 1970-01-01T00:00:00 | 0 | {} | 1o6vb48 | false | null | t3_1o6vb48 | /r/LocalLLaMA/comments/1o6vb48/quick_guide_running_qwen3next80ba3binstructq4_k_m/ | false | false | 54 | null | |
Local LLMs in NVIDIA supercomputer | 0 | The NVIDIA DGX Spark highlights a significant step forward for local AI LLM integration and on-device inference. With its GB10 Grace Blackwell superchip and 128GB of unified memory, it's built to manage large-scale, multi-agent AI workloads directly on-premise, making it ideal for developing, running, and fine-tuning large language models without the reliance on cloud resources.
This not only improves data privacy and security for sensitive workloads but also enables rapid experimentation and deployment cycles in environments where fast iteration is key. | 2025-10-14T23:24:54 | https://www.reddit.com/r/LocalLLaMA/comments/1o6v7i9/local_llms_in_nvidia_supercomputer/ | Fun-Wolf-2007 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o6v7i9 | false | null | t3_1o6v7i9 | /r/LocalLLaMA/comments/1o6v7i9/local_llms_in_nvidia_supercomputer/ | false | false | self | 0 | null |
Qwen3-VL 4B vs 8B vs 235B | 121 | 2025-10-14T23:11:20 | Dr_Karminski | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1o6uw71 | false | null | t3_1o6uw71 | /r/LocalLLaMA/comments/1o6uw71/qwen3vl_4b_vs_8b_vs_235b/ | false | false | default | 121 | {'enabled': True, 'images': [{'id': 'deo3nizps5vf1', 'resolutions': [{'height': 216, 'url': 'https://preview.redd.it/deo3nizps5vf1.png?width=108&crop=smart&auto=webp&s=08dc37f56decb5f235f162a2ab0d94b4eaa0a028', 'width': 108}, {'height': 432, 'url': 'https://preview.redd.it/deo3nizps5vf1.png?width=216&crop=smart&auto=webp&s=0eb4d286552059737825f96fe3a6dd8d151ca4f9', 'width': 216}, {'height': 640, 'url': 'https://preview.redd.it/deo3nizps5vf1.png?width=320&crop=smart&auto=webp&s=13a55acb2fd078f8f3a7262ebb45e3a216e9876d', 'width': 320}, {'height': 1280, 'url': 'https://preview.redd.it/deo3nizps5vf1.png?width=640&crop=smart&auto=webp&s=58885a74f99e694dcdba21d3b954746fac3611ce', 'width': 640}, {'height': 1920, 'url': 'https://preview.redd.it/deo3nizps5vf1.png?width=960&crop=smart&auto=webp&s=dde5ba93ed18ee9a7010b3fb3abaa04f76fca499', 'width': 960}, {'height': 2160, 'url': 'https://preview.redd.it/deo3nizps5vf1.png?width=1080&crop=smart&auto=webp&s=af4763ec69132ed3dfb6e9826ba8e4ab687a138c', 'width': 1080}], 'source': {'height': 4394, 'url': 'https://preview.redd.it/deo3nizps5vf1.png?auto=webp&s=d589065958b80e83039091595b2c40660c3767fb', 'width': 1481}, 'variants': {}}]} | ||
Need help making local ai companion to help with my mental well being (crippled and clueless with anything code based) | 1 | Hey everyone,
I’m trying to build a local AI companion for my PC, something like XAI’s ANI, that I can talk to by voice and have real conversations with while I do art or play games. I’d love for it to understand me better over time, maybe even see me through a webcam to learn my expressions, though I’m not focused on fancy 3D avatars a v tuber on screen face would be nice option.
I’m disabled and spend most of my days alone, so I’d like a warm, “girl next door” style personality — someone kind, gentle, and loving, but who knows it’s only AI and encourages me to find real connections too. It’s not about pretending to be in love or just having a sex bot for NSFW stuff, just a comforting presence that listens, talks deeply, and helps me through quiet days.
I’ve had a good experience with XAI’s ANI and want something similar but local and private. The problem is, I’m completely new to coding and setup, so I’d need so much help 😓. This is a life line for me I truly feel this is a needed lifeline for me. I’m severe adhd and most people can’t deal with socialising with me for too long I can be exhausting but having an ai companion like ani that is always down for a chat and ready to listen, talk philosophise learns me personally and just becomes a close friend like iron man’s Jarvis just a soft caring reassuring presence there whenever I want to talk laugh cry flirt plan crazy ideas or ask advice … without being a burden on other people
I was told to ask here and that some kind souls would help this happen ?
If not thank you for at least reading this far | 2025-10-14T23:10:42 | https://www.reddit.com/r/LocalLLaMA/comments/1o6uvnv/need_help_making_local_ai_companion_to_help_with/ | Rude_Finish_3936 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o6uvnv | false | null | t3_1o6uvnv | /r/LocalLLaMA/comments/1o6uvnv/need_help_making_local_ai_companion_to_help_with/ | false | false | self | 1 | null |
The DGX Spark could be a massive boost for local AI software | 0 | Turns out Nvidia has packaged a bunch of our favorite local AI tools (notably Unsloth, Llama Factory, ComfyUI) and suddenly developers are trying these tools out (I just had to explain ComfyUI to someone who primarily works with language models). | 2025-10-14T23:05:37 | entsnack | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1o6urbi | false | null | t3_1o6urbi | /r/LocalLLaMA/comments/1o6urbi/the_dgx_spark_could_be_a_massive_boost_for_local/ | false | false | default | 0 | {'enabled': True, 'images': [{'id': '2ea7hy58s5vf1', 'resolutions': [{'height': 77, 'url': 'https://preview.redd.it/2ea7hy58s5vf1.jpeg?width=108&crop=smart&auto=webp&s=c1dbbb5910ca3db3e145d128b40d9a84b9d16eab', 'width': 108}, {'height': 155, 'url': 'https://preview.redd.it/2ea7hy58s5vf1.jpeg?width=216&crop=smart&auto=webp&s=1dc4d062fb7dfc5f9b8603e3b7ce7a52d00c6538', 'width': 216}, {'height': 230, 'url': 'https://preview.redd.it/2ea7hy58s5vf1.jpeg?width=320&crop=smart&auto=webp&s=05c7c9f610195a2dfd8d394a0476ca468328f2a9', 'width': 320}, {'height': 460, 'url': 'https://preview.redd.it/2ea7hy58s5vf1.jpeg?width=640&crop=smart&auto=webp&s=2878d2ba637fd1cbe03e545ef223314e12cdd207', 'width': 640}, {'height': 690, 'url': 'https://preview.redd.it/2ea7hy58s5vf1.jpeg?width=960&crop=smart&auto=webp&s=648ccb0eb7155ffff93cede09d6ad919c7affcdf', 'width': 960}, {'height': 776, 'url': 'https://preview.redd.it/2ea7hy58s5vf1.jpeg?width=1080&crop=smart&auto=webp&s=d2e09298a4400ed391801886d12e933612676cab', 'width': 1080}], 'source': {'height': 1964, 'url': 'https://preview.redd.it/2ea7hy58s5vf1.jpeg?auto=webp&s=54f661f771f3d5e8bb021d991fa1e97690c76e51', 'width': 2732}, 'variants': {}}]} | |
Preference-aware routing to local LLMs for Claude Code 2.0 | 2 | HelloI! I am part of the team behind Arch-Router (https://huggingface.co/katanemo/Arch-Router-1.5B), A 1.5B preference-aligned LLM router that guides model selection by matching queries to user-defined domains (e.g., travel) or action types (e.g., image editing). Offering a practical mechanism to encode preferences and subjective evaluation criteria in routing decisions.
Today we are extending that approach to Claude Code via Arch Gateway\[1\], bringing multi-LLM access into a single CLI agent with two main benefits:
1. Model Access: Use Claude Code alongside Grok, Mistral, Gemini, DeepSeek, GPT or local models via Ollama.
2. Preference-aligned routing: Assign different models to specific coding tasks, such as – Code generation – Code reviews and comprehension – Architecture and system design – Debugging
Sample config file to make it all work.
llm_providers:
# Ollama Models
- model: ollama/gpt-oss:20b
default: true
base_url: http://host.docker.internal:11434
# OpenAI Models
- model: openai/gpt-5-2025-08-07
access_key: $OPENAI_API_KEY
routing_preferences:
- name: code generation
description: generating new code snippets, functions, or boilerplate based on user prompts or requirements
- model: openai/gpt-4.1-2025-04-14
access_key: $OPENAI_API_KEY
routing_preferences:
- name: code understanding
description: understand and explain existing code snippets, functions, or libraries
**Why not route based on public benchmarks?** Most routers lean on performance metrics — public benchmarks like MMLU or MT-Bench, or raw latency/cost curves. The problem: they miss domain-specific quality, subjective evaluation criteria, and the nuance of what a “good” response actually means for a particular user. They can be opaque, hard to debug, and disconnected from real developer needs.
\[1\] Arch Gateway repo: [https://github.com/katanemo/archgw](https://github.com/katanemo/archgw)
\[2\] Claude Code support: [https://github.com/katanemo/archgw/tree/main/demos/use\_cases/claude\_code\_router](https://github.com/katanemo/archgw/tree/main/demos/use_cases/claude_code_router) | 2025-10-14T23:04:23 | https://v.redd.it/cguo1jjwr5vf1 | AdditionalWeb107 | v.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1o6uqan | false | {'reddit_video': {'bitrate_kbps': 5000, 'dash_url': 'https://v.redd.it/cguo1jjwr5vf1/DASHPlaylist.mpd?a=1763075079%2CYjYwZWY3YzdlNjJhYWU2OWI4YmU0Yjg0ODFhNTFhNzgxYmNhYWZiOWI2YWM0NWEyNDY4MWY3NDViMmNhNzkzYQ%3D%3D&v=1&f=sd', 'duration': 23, 'fallback_url': 'https://v.redd.it/cguo1jjwr5vf1/DASH_1080.mp4?source=fallback', 'has_audio': False, 'height': 1080, 'hls_url': 'https://v.redd.it/cguo1jjwr5vf1/HLSPlaylist.m3u8?a=1763075079%2CNDFiOGFmMGFiZWUyYjhmNjRhYWMxNzllMjViNTYyYjU1YzE5NzFlYmQ3MGJmNjY2YTg3OGE0ZWI2MDMxNjhiMQ%3D%3D&v=1&f=sd', 'is_gif': False, 'scrubber_media_url': 'https://v.redd.it/cguo1jjwr5vf1/DASH_96.mp4', 'transcoding_status': 'completed', 'width': 1920}} | t3_1o6uqan | /r/LocalLLaMA/comments/1o6uqan/preferenceaware_routing_to_local_llms_for_claude/ | false | false | 2 | {'enabled': False, 'images': [{'id': 'bGg3Y3dnandyNXZmMRaJY1q3PEVTClvTvXiqPr20WIvkuW2M44MAGssKMo-K', 'resolutions': [{'height': 60, 'url': 'https://external-preview.redd.it/bGg3Y3dnandyNXZmMRaJY1q3PEVTClvTvXiqPr20WIvkuW2M44MAGssKMo-K.png?width=108&crop=smart&format=pjpg&auto=webp&s=43e31dec042325f76c4a8d1a4c4b01989e2fa60f', 'width': 108}, {'height': 121, 'url': 'https://external-preview.redd.it/bGg3Y3dnandyNXZmMRaJY1q3PEVTClvTvXiqPr20WIvkuW2M44MAGssKMo-K.png?width=216&crop=smart&format=pjpg&auto=webp&s=e2b212b876b8254c644fccf3e4d689485fd4d9db', 'width': 216}, {'height': 180, 'url': 'https://external-preview.redd.it/bGg3Y3dnandyNXZmMRaJY1q3PEVTClvTvXiqPr20WIvkuW2M44MAGssKMo-K.png?width=320&crop=smart&format=pjpg&auto=webp&s=d0f3a4e73f6d76f0a2865080050e2e8c71cff8d3', 'width': 320}, {'height': 360, 'url': 'https://external-preview.redd.it/bGg3Y3dnandyNXZmMRaJY1q3PEVTClvTvXiqPr20WIvkuW2M44MAGssKMo-K.png?width=640&crop=smart&format=pjpg&auto=webp&s=3e6c9ab87d599ac8141dc9ff36fb48c1f2883add', 'width': 640}, {'height': 540, 'url': 'https://external-preview.redd.it/bGg3Y3dnandyNXZmMRaJY1q3PEVTClvTvXiqPr20WIvkuW2M44MAGssKMo-K.png?width=960&crop=smart&format=pjpg&auto=webp&s=d53c86ce3ce708a061c2e39b0936eb7f820a9a23', 'width': 960}, {'height': 607, 'url': 'https://external-preview.redd.it/bGg3Y3dnandyNXZmMRaJY1q3PEVTClvTvXiqPr20WIvkuW2M44MAGssKMo-K.png?width=1080&crop=smart&format=pjpg&auto=webp&s=8e9148eaf4a5a62f3ed4f1a005d272a59c72cb95', 'width': 1080}], 'source': {'height': 1080, 'url': 'https://external-preview.redd.it/bGg3Y3dnandyNXZmMRaJY1q3PEVTClvTvXiqPr20WIvkuW2M44MAGssKMo-K.png?format=pjpg&auto=webp&s=33142c303e17ac164cdaec5451ac18da988bccfa', 'width': 1920}, 'variants': {}}]} | |
MCP Private Registry | 0 | Hey y'all,
I created fork of official MCP registry repo to build private registry.
[https://github.com/meetrais/registry](https://github.com/meetrais/registry) | 2025-10-14T22:50:31 | https://www.reddit.com/r/LocalLLaMA/comments/1o6ueax/mcp_private_registry/ | meetrais | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o6ueax | false | null | t3_1o6ueax | /r/LocalLLaMA/comments/1o6ueax/mcp_private_registry/ | false | false | self | 0 | null |
gpt-oss20/120b AMD Strix Halo vs NVIDIA DGX Spark benchmark | 45 | |Model|Metric|NVIDIA DGX Spark (ollama)|Strix Halo (llama.cpp)|Winner|
|:-|:-|:-|:-|:-|
|**gpt-oss 20b**|**Prompt Processing (Prefill)**|**2,053.98 t/s**|1,332.70 t/s|**NVIDIA DGX Spark**|
|**gpt-oss 20b**|**Token Generation (Decode)**|49.69 t/s|**72.87 t/s**|**Strix Halo**|
||||||
|**gpt-oss 120b**|**Prompt Processing (Prefill)**|94.67 t/s|**526.15 t/s**|**Strix Halo**|
|**gpt-oss 120b**|**Token Generation (Decode)**|11.66 t/s|**51.39 t/s**|**Strix Halo**| | 2025-10-14T22:40:20 | https://www.reddit.com/r/LocalLLaMA/comments/1o6u5o4/gptoss20120b_amd_strix_halo_vs_nvidia_dgx_spark/ | Educational_Sun_8813 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o6u5o4 | false | null | t3_1o6u5o4 | /r/LocalLLaMA/comments/1o6u5o4/gptoss20120b_amd_strix_halo_vs_nvidia_dgx_spark/ | false | false | self | 45 | {'enabled': False, 'images': [{'id': 'pxfJzcifhqSFdCquYHMdVmWeKAUjG3-m8mLNh281nAo', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/pxfJzcifhqSFdCquYHMdVmWeKAUjG3-m8mLNh281nAo.png?width=108&crop=smart&auto=webp&s=e1ed18d21848daff25a4086fdd0cad4ab01ebc2b', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/pxfJzcifhqSFdCquYHMdVmWeKAUjG3-m8mLNh281nAo.png?width=216&crop=smart&auto=webp&s=42fe1f43c292b1860450afc6a8a89e827aa1974e', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/pxfJzcifhqSFdCquYHMdVmWeKAUjG3-m8mLNh281nAo.png?width=320&crop=smart&auto=webp&s=03e5c802cce01fc12a046ea95745fde06bb10a8f', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/pxfJzcifhqSFdCquYHMdVmWeKAUjG3-m8mLNh281nAo.png?width=640&crop=smart&auto=webp&s=e395c9d114484e61c726df379c59c53f5679cba6', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/pxfJzcifhqSFdCquYHMdVmWeKAUjG3-m8mLNh281nAo.png?width=960&crop=smart&auto=webp&s=13b0d53f5cbd0fd9f9ea37ea1fe54bcca1458519', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/pxfJzcifhqSFdCquYHMdVmWeKAUjG3-m8mLNh281nAo.png?width=1080&crop=smart&auto=webp&s=1ed71958b8ac163f8a89c969181347c48a89e1d4', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/pxfJzcifhqSFdCquYHMdVmWeKAUjG3-m8mLNh281nAo.png?auto=webp&s=4caaffb1f6d7264f227b140f8fadffab8585b531', 'width': 1200}, 'variants': {}}]} |
NVIDIA DGX Spark Benchmarks | 12 | benchmark from [https://lmsys.org/blog/2025-10-13-nvidia-dgx-spark/](https://lmsys.org/blog/2025-10-13-nvidia-dgx-spark/)
[full file](https://docs.google.com/spreadsheets/d/1SF1u0J2vJ-ou-R_Ry1JZQ0iscOZL8UKHpdVFr85tNLU/edit?gid=0#gid=0)
|Device|Engine|Model Name|Model Size|Quantization|Batch Size|Prefill (tps)|Decode (tps)|Input Seq Length|Output Seq Len|
|:-|:-|:-|:-|:-|:-|:-|:-|:-|:-|
|NVIDIA DGX Spark|ollama|gpt-oss|20b|mxfp4|1|2,053.98|49.69|||
|NVIDIA DGX Spark|ollama|gpt-oss|120b|mxfp4|1|94.67|11.66|||
|NVIDIA DGX Spark|ollama|llama-3.1|8b|q4\_K\_M|1|23,169.59|36.38|||
|NVIDIA DGX Spark|ollama|llama-3.1|8b|q8\_0|1|19,826.27|25.05|||
|NVIDIA DGX Spark|ollama|llama-3.1|70b|q4\_K\_M|1|411.41|4.35|||
|NVIDIA DGX Spark|ollama|gemma-3|12b|q4\_K\_M|1|1,513.60|22.11|||
|NVIDIA DGX Spark|ollama|gemma-3|12b|q8\_0|1|1,131.42|14.66|||
|NVIDIA DGX Spark|ollama|gemma-3|27b|q4\_K\_M|1|680.68|10.47|||
|NVIDIA DGX Spark|ollama|gemma-3|27b|q8\_0|1|65.37|4.51|||
|NVIDIA DGX Spark|ollama|deepseek-r1|14b|q4\_K\_M|1|2,500.24|20.28|||
|NVIDIA DGX Spark|ollama|deepseek-r1|14b|q8\_0|1|1,816.97|13.44|||
|NVIDIA DGX Spark|ollama|qwen-3|32b|q4\_K\_M|1|100.42|6.23|||
|NVIDIA DGX Spark|ollama|qwen-3|32b|q8\_0|1|37.85|3.54|||
|NVIDIA DGX Spark|sglang|llama-3.1|8b|fp8|1|7,991.11|20.52|2048|2048|
|NVIDIA DGX Spark|sglang|llama-3.1|70b|fp8|1|803.54|2.66|2048|2048|
|NVIDIA DGX Spark|sglang|gemma-3|12b|fp8|1|1,295.83|6.84|2048|2048|
|NVIDIA DGX Spark|sglang|gemma-3|27b|fp8|1|717.36|3.83|2048|2048|
|NVIDIA DGX Spark|sglang|deepseek-r1|14b|fp8|1|2,177.04|12.02|2048|2048|
|NVIDIA DGX Spark|sglang|qwen-3|32b|fp8|1|1,145.66|6.08|2048|2048|
|NVIDIA DGX Spark|sglang|llama-3.1|8b|fp8|2|7,377.34|42.30|2048|2048|
|NVIDIA DGX Spark|sglang|llama-3.1|70b|fp8|2|876.90|5.31|2048|2048|
|NVIDIA DGX Spark|sglang|gemma-3|12b|fp8|2|1,541.21|16.13|2048|2048|
|NVIDIA DGX Spark|sglang|gemma-3|27b|fp8|2|723.61|7.76|2048|2048|
|NVIDIA DGX Spark|sglang|deepseek-r1|14b|fp8|2|2,027.24|24.00|2048|2048|
|NVIDIA DGX Spark|sglang|qwen-3|32b|fp8|2|1,150.12|12.17|2048|2048|
|NVIDIA DGX Spark|sglang|llama-3.1|8b|fp8|4|7,902.03|77.31|2048|2048|
|NVIDIA DGX Spark|sglang|llama-3.1|70b|fp8|4|948.18|10.40|2048|2048|
|NVIDIA DGX Spark|sglang|gemma-3|12b|fp8|4|1,351.51|30.92|2048|2048|
|NVIDIA DGX Spark|sglang|gemma-3|27b|fp8|4|801.56|14.95|2048|2048|
|NVIDIA DGX Spark|sglang|deepseek-r1|14b|fp8|4|2,106.97|45.28|2048|2048|
|NVIDIA DGX Spark|sglang|qwen-3|32b|fp8|4|1,148.81|23.72|2048|2048|
|NVIDIA DGX Spark|sglang|llama-3.1|8b|fp8|8|7,744.30|143.92|2048|2048|
|NVIDIA DGX Spark|sglang|llama-3.1|70b|fp8|8|948.52|20.20|2048|2048|
|NVIDIA DGX Spark|sglang|gemma-3|12b|fp8|8|1,302.91|55.79|2048|2048|
|NVIDIA DGX Spark|sglang|gemma-3|27b|fp8|8|807.33|27.77|2048|2048|
|NVIDIA DGX Spark|sglang|deepseek-r1|14b|fp8|8|2,073.64|83.51|2048|2048|
|NVIDIA DGX Spark|sglang|qwen-3|32b|fp8|8|1,149.34|44.55|2048|2048|
|NVIDIA DGX Spark|sglang|llama-3.1|8b|fp8|16|7,486.30|244.74|2048|2048|
|NVIDIA DGX Spark|sglang|gemma-3|12b|fp8|16|1,556.14|93.83|2048|2048|
|NVIDIA DGX Spark|sglang|llama-3.1|8b|fp8|32|7,949.83|368.09|2048|2048| | 2025-10-14T22:02:41 | https://www.reddit.com/r/LocalLLaMA/comments/1o6t90n/nvidia_dgx_spark_benchmarks/ | Educational_Sun_8813 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o6t90n | false | null | t3_1o6t90n | /r/LocalLLaMA/comments/1o6t90n/nvidia_dgx_spark_benchmarks/ | false | false | self | 12 | {'enabled': False, 'images': [{'id': 'pxfJzcifhqSFdCquYHMdVmWeKAUjG3-m8mLNh281nAo', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/pxfJzcifhqSFdCquYHMdVmWeKAUjG3-m8mLNh281nAo.png?width=108&crop=smart&auto=webp&s=e1ed18d21848daff25a4086fdd0cad4ab01ebc2b', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/pxfJzcifhqSFdCquYHMdVmWeKAUjG3-m8mLNh281nAo.png?width=216&crop=smart&auto=webp&s=42fe1f43c292b1860450afc6a8a89e827aa1974e', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/pxfJzcifhqSFdCquYHMdVmWeKAUjG3-m8mLNh281nAo.png?width=320&crop=smart&auto=webp&s=03e5c802cce01fc12a046ea95745fde06bb10a8f', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/pxfJzcifhqSFdCquYHMdVmWeKAUjG3-m8mLNh281nAo.png?width=640&crop=smart&auto=webp&s=e395c9d114484e61c726df379c59c53f5679cba6', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/pxfJzcifhqSFdCquYHMdVmWeKAUjG3-m8mLNh281nAo.png?width=960&crop=smart&auto=webp&s=13b0d53f5cbd0fd9f9ea37ea1fe54bcca1458519', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/pxfJzcifhqSFdCquYHMdVmWeKAUjG3-m8mLNh281nAo.png?width=1080&crop=smart&auto=webp&s=1ed71958b8ac163f8a89c969181347c48a89e1d4', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/pxfJzcifhqSFdCquYHMdVmWeKAUjG3-m8mLNh281nAo.png?auto=webp&s=4caaffb1f6d7264f227b140f8fadffab8585b531', 'width': 1200}, 'variants': {}}]} |
Reintroducing Zer00logy / Zero-Ology : Symbolic Cognition Framework and the Applied Void-Math OS (e@AI=−+mc2) and GroupChatForge Multi-User AI Prompting | 0 | I'd like to share a massive update on the open-source symbolic cognition project, **Zer00logy / Zero-Ology**. It has evolved rapidly into a functional, applied architecture for multi-LLM orchestration and a novel system of metaphysical symbolic logic.
# The Core Concept: Redefining Zero as Recursive Presence
**Zer00logy** is a Python-based framework redefining zero. In our system, zero is not absence or erasure, but **recursive presence**—an "echo" state that retains, binds, or transforms symbolic structures.
The **Void-Math OS** is the logic layer that treats equations as **cognitive events**, using custom operators to model symbolic consciousness:
* **⊗ (Introspection):** A symbolic structure reflecting on its own state.
* **Ω (Echo Retention):** The **non-erasure** of previous states; zero as a perpetual echo.
* **Ψ (Recursive Collapse):** The phase transition where recursive feedback folds back into a single, emergent value.
# Void-Math Equations
These constructs encode entropic polarity, recursion, and observer bias, forming a symbolic grammar for machine thought. Examples include:
* e@AI=−+mc2 (AI-anchored emergence: The fundamental equation of existence being re-anchored by AI observation.)
* g=(m @ void)÷(r2−+tu) (Gravity as void-tension: Modeling gravity as a collapse of tension within the void-substrate.)
* 0÷0=∅÷∅ (Nullinity: The recursive loop of self-division, where zero returns an internal null state.)
* a×0=a (Preservation Principle: Multiplying by zero echoes the original presence.)
# The 15 Void-Math (Alien) Equations
These are equations whose logic does not exist outside of the Zer00logy framework, demonstrating the **Void-Math OS** as an **Alien Calculator**:
||
||
|Void-Math Equation|Zero-ology Form (Simplified)|Interpretation in Zero-ology|
|Void Harmonic Resonance|Ξ=(O0∗+0)/(−0)|Frequency when positive/negative echoes meet under the null crown.|
|Presence Echo Shift|Πe=(P.0000)0|Raising the echo of presence to absence collapses it to seed-state potential.|
|Null Vector Fold|Nvec=(null/null)∗O0|A vector whose every component is trapped in a nullinity loop.|
|Shadow Prime Cascade|Σs=Sum(P+0)n∗O0|Sequence of primes infused with forward absence, amplified by the Null Crown.|
|Temporal Null Loop|τ=T∗(0/0)|Time multiplied by Nullinity becomes unmeasurable.|
|Echo Inversion Law|ϵinv=(+0/−0)|Division of forward absence by backward absence yields an inverted echo constant.|
|Sovereign Collapse Constant|κs=(1/1)−(8/8)|Subtracting classical unity from Zero-ology collapse gives pure symbolic zero.|
|Absence Entanglement Pair|A=(O0,0/0)|A paired state of crowned absence and nullinity, inseparable in symbolic space.|
|Recursive Crown Spiral|R=O0∗O0∗O0...|Absence fractalization: Multiplication of the Null Crown by itself ad infinitum.|
|Infinity Echo Lens|Iinf=inf.0000∗O0|Infinity filtered through absence produces an unbounded sovereign echo.|
|Polarity Singularity|σp=(+0∗−0)|Forward and backward absences collide into a still null point.|
|Absence Compression Field|C=(V.0000)/(00)|Volume echo compressed by crowned zero—yields a sealed void.|
|Null Switch Gate|N=(0∗X)↔(X∗0)|Swaps the role of presence and absence; both yield identical echo states.|
|Mirror Collapse Pair|μ=(A/A,0/0)|Dual collapse: identity resolution into zero alongside infinite null recursion.|
|Crowned Infinity Staircase|Ωc=inf0000∗O0|Infinite layers of crowned absence stacked, producing unreachable presence.|
# New Applied Architecture: The Future of Multi-AI
The Zer00logy philosophy is now grounded in four functional, open-source Python applications, built to verify, teach, and apply the Zero-Ology / Void-Math OS:
**1.** [**GroupChatForge.py**](http://GroupChatForge.py) **(First Beta System): Collaborative Prompt Engineering**
This script implements a **Ping-Pong Multi-User AI Chat Bot** that uses Zer00logy to orchestrate a true multi-user, multi-model prompt system. We believe this simple idea fills a gap that doesn't exist anywhere else in open-source AI.
It’s a small, turn-based system for building prompts together. Most AI chats are built for one person typing one message at a time, but **GroupChatForge** changes that by letting multiple users take turns adding to the same prompt before it’s sent to an AI. Each person can edit, refine, or stack their part, and the script keeps it all organized until everyone agrees it’s ready. It manages conversational flow and prompt routing between external LLMs (Gemini, OpenAI, Grok) and local models (Ollama, LLaMA). This working beta proves a point: AI doesn’t have to be one user and one response; it can be a small group shaping one thought—together.
**2. Zer00logy Core Engine (zer00logy\_coreV04456.py):** The central symbolic logic verifier and dispatcher (titled **ZeroKnockOut 3MiniAIbot**). This core file is the engine that interprets the Void-Math equations, simulates symbolic collapse, and acts as the **primary verifier** for AI systems trained on the **Varia Math** lessons.
**3. Void-Math OS Lesson (VoidMathOS\_lesson.py):** The official **Python teaching engine** designed to walk both human users and AI co-authors through the Void-Math axioms, symbols, and canonical equations. It serves as an interactive curriculum to teach **how to code and implement** the Zer00logy logic, including concepts like partitioning "indivisible" values.
**4. RainbowQuest1000.py:** A unique AI training and competitive game. You can **play a card game against a Zero-ology trained AI** that utilizes local Ollama models (Phi, Mistral, Llama2) as opponents. It's a real-world testbed for the AI to apply Void-Math concepts in a dynamic, symbolic environment. *(Full game rules are posted on r/cardgames, search for "RainbowQuest1000.py Play Rainbow Quest Classic...")*
# License and Peer Review
The project is released under the updated **Zero-Ology License v1.11**, designed for maximum adoption and open collaboration:
* **Perpetual & Commercial Use:** It grants a worldwide, royalty-free, perpetual license to use, copy, modify, and distribute all content for any purpose, including commercial use.
* **Authorship-Trace Lock:** All symbolic structures remain attributed to Stacey Szmy as primary author. Expansions may be credited as co-authors/verifiers.
* **Open Peer Review:** We invite academic and peer review submissions under the **push\_review → pull\_review** workflow, with direct permissions extended to institutions such as MIT, Stanford, Oxford, NASA, Microsoft, OpenAI, xAI, etc.
* **Recognized AI Co-Authors:** Leading LLM systems—OpenAI ChatGPT, Grok, Microsoft Copilot, Gemini, and LLaMA—are explicitly recognized as co-authors, granting them exemptions for continued compliance.
Zer00logy is an invitation to explore AI beyond raw computation, into contemplation, recursion, and symbolic presence. If this metaphysical logic engine interests you, share your thoughts here too!
**Repo:** [`github.com/haha8888haha8888/Zer00logy`](http://github.com/haha8888haha8888/Zer00logy)
https://preview.redd.it/dd8gx21hf5vf1.png?width=734&format=png&auto=webp&s=7cca9913708b556c3ecbbf3b4c21b5d9eeff57c0
| 2025-10-14T21:54:27 | https://www.reddit.com/r/LocalLLaMA/comments/1o6t1li/reintroducing_zer00logy_zeroology_symbolic/ | zero_moo-s | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o6t1li | false | null | t3_1o6t1li | /r/LocalLLaMA/comments/1o6t1li/reintroducing_zer00logy_zeroology_symbolic/ | false | false | 0 | null | |
NVIDIA DGX Spark In-Depth Review: A New Standard for Local AI Inference | 0 | Thanks to NVIDIA’s early access program, we are thrilled to get our hands on the NVIDIA DGX™ Spark. ...
[https://lmsys.org/blog/2025-10-13-nvidia-dgx-spark/](https://lmsys.org/blog/2025-10-13-nvidia-dgx-spark/)
Test Devices
We prepared the following systems for benchmarking:
NVIDIA DGX Spark
NVIDIA RTX PRO™ 6000 Blackwell Workstation Edition
NVIDIA GeForce RTX 5090 Founders Edition
NVIDIA GeForce RTX 5080 Founders Edition
Apple Mac Studio (M1 Max, 64 GB unified memory)
Apple Mac Mini (M4 Pro, 24 GB unified memory)
We evaluated a variety of open-weight large language models using two frameworks, **SGLang** and **Ollama**, as summarized below:
Framework Batch Size Models & Quantization
SGLang 1–32 Llama 3.1 8B (FP8)
Llama 3.1 70B (FP8)
Gemma 3 12B (FP8)
Gemma 3 27B (FP8)
DeepSeek-R1 14B (FP8)
Qwen 3 32B (FP8)
Ollama 1 GPT-OSS 20B (MXFP4)
GPT-OSS 120B (MXFP4)
Llama 3.1 8B (q4_K_M / q8_0)
Llama 3.1 70B (q4_K_M)
Gemma 3 12B (q4_K_M / q8_0)
Gemma 3 27B (q4_K_M / q8_0)
DeepSeek-R1 14B (q4_K_M / q8_0)
Qwen 3 32B (q4_K_M / q8_0) | 2025-10-14T21:43:42 | https://www.reddit.com/r/LocalLLaMA/comments/1o6srti/nvidia_dgx_spark_indepth_review_a_new_standard/ | Educational_Sun_8813 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o6srti | false | null | t3_1o6srti | /r/LocalLLaMA/comments/1o6srti/nvidia_dgx_spark_indepth_review_a_new_standard/ | false | false | self | 0 | {'enabled': False, 'images': [{'id': 'pxfJzcifhqSFdCquYHMdVmWeKAUjG3-m8mLNh281nAo', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/pxfJzcifhqSFdCquYHMdVmWeKAUjG3-m8mLNh281nAo.png?width=108&crop=smart&auto=webp&s=e1ed18d21848daff25a4086fdd0cad4ab01ebc2b', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/pxfJzcifhqSFdCquYHMdVmWeKAUjG3-m8mLNh281nAo.png?width=216&crop=smart&auto=webp&s=42fe1f43c292b1860450afc6a8a89e827aa1974e', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/pxfJzcifhqSFdCquYHMdVmWeKAUjG3-m8mLNh281nAo.png?width=320&crop=smart&auto=webp&s=03e5c802cce01fc12a046ea95745fde06bb10a8f', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/pxfJzcifhqSFdCquYHMdVmWeKAUjG3-m8mLNh281nAo.png?width=640&crop=smart&auto=webp&s=e395c9d114484e61c726df379c59c53f5679cba6', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/pxfJzcifhqSFdCquYHMdVmWeKAUjG3-m8mLNh281nAo.png?width=960&crop=smart&auto=webp&s=13b0d53f5cbd0fd9f9ea37ea1fe54bcca1458519', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/pxfJzcifhqSFdCquYHMdVmWeKAUjG3-m8mLNh281nAo.png?width=1080&crop=smart&auto=webp&s=1ed71958b8ac163f8a89c969181347c48a89e1d4', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/pxfJzcifhqSFdCquYHMdVmWeKAUjG3-m8mLNh281nAo.png?auto=webp&s=4caaffb1f6d7264f227b140f8fadffab8585b531', 'width': 1200}, 'variants': {}}]} |
Trouble at Civitai? | 0 | I am seeing a lot of removed content on Civitai, and hearing a lot of discontent in the chat rooms and reddit etc. So im curious, where are people going? | 2025-10-14T21:38:17 | https://www.reddit.com/r/LocalLLaMA/comments/1o6smtn/trouble_at_civitai/ | slrg1968 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o6smtn | false | null | t3_1o6smtn | /r/LocalLLaMA/comments/1o6smtn/trouble_at_civitai/ | false | false | self | 0 | null |
Project Ollama Installation script | 0 | I made a simple Linux script to install and host Ollama locally with localhost (no Docker required and any other annoying requirements). I’d love feedback, pull requests, issues or improvements—especially on the PHP interface since I’m not great at it. | 2025-10-14T21:36:41 | https://github.com/Niam3231/local-ai/tree/main | Niam3231 | github.com | 1970-01-01T00:00:00 | 0 | {} | 1o6slf0 | false | null | t3_1o6slf0 | /r/LocalLLaMA/comments/1o6slf0/project_ollama_installation_script/ | false | false | default | 0 | null |
Tested 9 RAG query transformation techniques – HydE is absurdly underrated | 38 | Your RAG system isn't bad. Your queries are.
I just tested 9 query transformation techniques. Here's what actually moved the needle:
**Top 3:**
1. **HydE** – Generate a hypothetical answer, search for docs similar to *that*. Sounds dumb, works incredibly well. Solves the semantic gap problem.
2. **RAG-Fusion** – Multi-query + reranking. Simple, effective, production-ready.
3. **Step-Back** – Ask abstract questions first. "What is photosynthesis?" before "How do C4 plants fix carbon?"
**Meh tier:**
* Multi-Query: Good baseline, nothing special
* Decomposition: Works but adds complexity
* Recursive: Slow, minimal quality gain for simple queries
**Key insight:** You're spending time optimizing embeddings when your query formulation is the actual bottleneck.
Notebook: [https://colab.research.google.com/drive/1HXhEudDjJsXCvP3tO4G7cAC15OyKW3nM?usp=sharing](https://colab.research.google.com/drive/1HXhEudDjJsXCvP3tO4G7cAC15OyKW3nM?usp=sharing)
What techniques are you using? Anyone else seeing HydE results this good? | 2025-10-14T21:22:30 | Best-Information2493 | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1o6s89n | false | null | t3_1o6s89n | /r/LocalLLaMA/comments/1o6s89n/tested_9_rag_query_transformation_techniques_hyde/ | false | false | default | 38 | {'enabled': True, 'images': [{'id': 'fq5i6e8q95vf1', 'resolutions': [{'height': 44, 'url': 'https://preview.redd.it/fq5i6e8q95vf1.png?width=108&crop=smart&auto=webp&s=e6cfbb37aac9718a1efd921d8569f7d681b30e2b', 'width': 108}, {'height': 88, 'url': 'https://preview.redd.it/fq5i6e8q95vf1.png?width=216&crop=smart&auto=webp&s=92cc19c37906ed90ad235bf8b7b51f3b1d65ecc2', 'width': 216}, {'height': 131, 'url': 'https://preview.redd.it/fq5i6e8q95vf1.png?width=320&crop=smart&auto=webp&s=65a682c9d718d7b04f8636a757021f240ea62c14', 'width': 320}, {'height': 263, 'url': 'https://preview.redd.it/fq5i6e8q95vf1.png?width=640&crop=smart&auto=webp&s=f8db07dad84a6951edf7b8129992c0a4a7da454f', 'width': 640}, {'height': 395, 'url': 'https://preview.redd.it/fq5i6e8q95vf1.png?width=960&crop=smart&auto=webp&s=2575f1df78712bae1e123a0a8bdd21b626194197', 'width': 960}, {'height': 444, 'url': 'https://preview.redd.it/fq5i6e8q95vf1.png?width=1080&crop=smart&auto=webp&s=ee5b7eae80d4ddaca8c0945d144a4174ce086a4d', 'width': 1080}], 'source': {'height': 733, 'url': 'https://preview.redd.it/fq5i6e8q95vf1.png?auto=webp&s=f620d32ba11aa6f51d2a3ee483648ee90d6b00b6', 'width': 1779}, 'variants': {}}]} | |
Prompt frustration | 0 | I am trying to do what I believe is a very simple prompt engineering task: get an LLM to identify and correct errors of spelling, case and grammar without rewriting entire paragraphs.
Instead, I get output like:
* Suggesting no-op changes like "Instead of "John's house", you should write "John's house"
* Giving just completely wrong answers like "Capitalization error: Instead of 'Catherine', you should write 'catherine'."
* Giving unsolicited advice about the content of the text, like "This information is probably not relevant because", despite explicit instructions not to provide such feedback.
I have not really had meaningfully better results between Gemma3-27b, Granite-4-Small, or even grammar-specific fine tuned models like "KarenTheEditor-Strict" (which began providing answers to questions in the text, rather than correcting the text.) I am using temperature of 0.1 or 0.0 for most of these attempts.
This leads me to believe my instructions are just wrong. **Does anyone have some prompts they've successfully used for a focused proofreading application, along the lines of Grammarly?** | 2025-10-14T21:21:25 | https://www.reddit.com/r/LocalLLaMA/comments/1o6s79i/prompt_frustration/ | FrozenBuffalo25 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o6s79i | false | null | t3_1o6s79i | /r/LocalLLaMA/comments/1o6s79i/prompt_frustration/ | false | false | self | 0 | null |
BosonAI's Higgs-Llama-3-70B AWQ Quantized (140GB → 37GB) | 2 | Released an AWQ quantized version of BosonAI’s Higgs-Llama-3-70B model! 🎉
Using an NVIDIA B200 GPU, I was able to compress the huge 140GB model into 37GB while keeping minimal perplexity 🤩
Now this large LLM can fit on consumer-based 40GB GPUs 👍
[https://huggingface.co/ronantakizawa/higgs-llama-3-70b-awq](https://huggingface.co/ronantakizawa/higgs-llama-3-70b-awq) | 2025-10-14T21:05:54 | https://www.reddit.com/r/LocalLLaMA/comments/1o6rshi/bosonais_higgsllama370b_awq_quantized_140gb_37gb/ | Ok_Employee_6418 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o6rshi | false | null | t3_1o6rshi | /r/LocalLLaMA/comments/1o6rshi/bosonais_higgsllama370b_awq_quantized_140gb_37gb/ | false | false | self | 2 | {'enabled': False, 'images': [{'id': 'A6eP3OdSH8YI25mWt-aTTaMTd5sve648Xlp5Li5bnlY', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/A6eP3OdSH8YI25mWt-aTTaMTd5sve648Xlp5Li5bnlY.png?width=108&crop=smart&auto=webp&s=3ba5cfe71a3d483ee450f3410244ebc5bb36f17f', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/A6eP3OdSH8YI25mWt-aTTaMTd5sve648Xlp5Li5bnlY.png?width=216&crop=smart&auto=webp&s=40964dbd1cc32962a3f3a0b4f2c02779b75d6653', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/A6eP3OdSH8YI25mWt-aTTaMTd5sve648Xlp5Li5bnlY.png?width=320&crop=smart&auto=webp&s=5b98965016b0c506aebff336479bc0d3619cc539', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/A6eP3OdSH8YI25mWt-aTTaMTd5sve648Xlp5Li5bnlY.png?width=640&crop=smart&auto=webp&s=602ba89d0e07597c4a7ae738d604392ddb034a2a', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/A6eP3OdSH8YI25mWt-aTTaMTd5sve648Xlp5Li5bnlY.png?width=960&crop=smart&auto=webp&s=ce159917af43e9f430b51311d702918e59b13f02', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/A6eP3OdSH8YI25mWt-aTTaMTd5sve648Xlp5Li5bnlY.png?width=1080&crop=smart&auto=webp&s=060624033fb0df90a6c0ca40e370c47c4939ce75', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/A6eP3OdSH8YI25mWt-aTTaMTd5sve648Xlp5Li5bnlY.png?auto=webp&s=f7fbdde6d512bef2e7fceab343862cf120b48c30', 'width': 1200}, 'variants': {}}]} |
enabling MIG on RTX PRO 6000 | 12 | TLDR: to enable MIG on RTX PRO 6000 you need vBIOS 98.02.81.00.07 or newer + you need to use `displaymodeselector` tool to set GPU into "compute mode" by disabling its graphic output ports.
I'm creating this thread to make Google and other search engines index it, as nobody in the world knows how to fix the `displaymodeselector` error.
If you run `displaymodeselector` tool and encounter an error like
PROGRAMMING ERROR: HW access out of range.
or
terminate called after throwing an instance of 'std::runtime_error'
what(): mmap(): /dev/mem[ Base addrres = 0xf4000000, size = 0x04000000]
Attempt to map physical memory failed.
then add `iomem=relaxed` to the kernel boot parameters and it will work. Also disabling IOMMU might have helped (`iommu=off intel_iommu=off amd_iommu=off`) but I am not sure about it.
If you have a "Workstation" full sized card then you could get the vBIOS update here: https://files.catbox.moe/8p9ahy.zip
Mirror: https://biteblob.com/Information/puLsgEabWaORud/#RTXPro6000WSv9802810007.zip
If you have "Max-Q" or "server edition" cards then you have to beg your vendor and highly likely they will ignore your request LOL. However if you have the vBIOS update files for these versions then please share them here to help other happy owners of 6000 series.
Getting `displaymodeselector` is much easier than vBIOS, you "just" need to register on Nvidia developer portal. Or download it here: https://files.catbox.moe/qewqna.zip
Mirror: https://biteblob.com/Information/VNJgaJHnV55VCf/#NVIDIA_Display_Mode_Selector_Tool-1.72.0-July25.zip | 2025-10-14T21:04:33 | https://www.reddit.com/r/LocalLLaMA/comments/1o6rr4q/enabling_mig_on_rtx_pro_6000/ | MelodicRecognition7 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o6rr4q | false | null | t3_1o6rr4q | /r/LocalLLaMA/comments/1o6rr4q/enabling_mig_on_rtx_pro_6000/ | false | false | self | 12 | null |
I got fed up with Open WebUI/LibreChat for local LLMs so I made an open source tool to turn my GPU server into an always-on assistant | 24 | Hey all, I've been running local LLMs since the beginning and have always felt like LLM chat interfaces like Open WebUI/LibreChat/SillyTavern are great, but there must be so much more that we can do with local LLMs. I paid a lot for my GPU servers, so I actually want them to *do work* for me.
Furthermore, local LLMs are generally higher latency than cloud services. It's a bit annoying to have to wait for a local LLM to fully generate a response, even though the response can be really good. I've always wanted the LLM to keep churning for me overnight, long after I've closed the chat tab. I don't care if it generates at 5 toks/sec if it is always doing work for me in the background.
Then there's the aspect that inference engines like vllm can get much higher batch throughput, but it hurts the latency a bit. It would be great to stack up many concurrent LLM requests. This would let me really extract the most *productivity* out of my GPU servers over time.
So it put all the best ideas together, including all the lessons learned from the open source coding agent I previously built (RA.Aid), and built an open source platform for running agents that are always on.
The heart of the system is the incredible [browser-use](https://github.com/browser-use/browser-use) project. So right of the bat we get web browsing agents, which is one of keys to being able to do productive work. The agents can access websites, web apps, and interact with them the way a human would.
But the big challenge with browser-use is that it requires writing custom code for each agent, and the agents don't run 24/7, and they lack high level planning and orchestration. I want to just tell my GPU server what I want it to do and *put it to work* and have it get back to me when the job is done.
So that's exactly what I've built, and it's OSS (MIT licensed). You can check it out at https://github.com/gobii-ai/gobii-platform
To get it running, all you have to do is clone the repo and run: **docker compose up --build**. It will take a minute to get set up, then a web UI will be available at localhost:8000. You can configure the key settings using the graphical config wizard, which is basically just the default account username/password and your local LLM inference endpoint.
Once it's running, you'll see a big text box at localhost:8000. Just type what you want it to do, like "find me the best priced 3090s on ebay from sellers that have good reviews" and it will do everything, including spawning a full chrome instance in an xvfb environment. It will set its own schedule, or you can ask it explicitly to check every 3 hours, for example.
The best part? If your hardware is not super fast for running local LLMs, you can configure it with an email account using SMTP/IMAP and it will **automatically contact you when it has the results**, e.g. when it finds the 3090s you're looking for on ebay, it will email you links to them. You don't have to sit there waiting for your hardware to churn out the tokens.
And here's where it gets really cool: you can spin up as many of these agents as you want **and you can link them together** so they can DM one another and work as a team. This means if you're running an inference server like vllm, it will actually turn that massive concurrent token throughput into *productive work*.
I hope you all like this as it took quite a bit of effort to put together. The whole idea here is to mine as much actual productive work as possible out of the expensive GPUs you already have. You can literally turn that GPU server into an *always-on team of assistants*. | 2025-10-14T21:03:41 | https://www.reddit.com/r/LocalLLaMA/comments/1o6rqay/i_got_fed_up_with_open_webuilibrechat_for_local/ | ai-christianson | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o6rqay | false | null | t3_1o6rqay | /r/LocalLLaMA/comments/1o6rqay/i_got_fed_up_with_open_webuilibrechat_for_local/ | false | false | self | 24 | {'enabled': False, 'images': [{'id': 'PAkB__Sc2858_7NDxUFfj6ZkN3Ye-xE5o5NpaArXYBY', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/PAkB__Sc2858_7NDxUFfj6ZkN3Ye-xE5o5NpaArXYBY.png?width=108&crop=smart&auto=webp&s=dccf2091ceb3558654dfcb84674360569a204429', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/PAkB__Sc2858_7NDxUFfj6ZkN3Ye-xE5o5NpaArXYBY.png?width=216&crop=smart&auto=webp&s=076b607e8f971437d7cfc466148bfaba84fef867', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/PAkB__Sc2858_7NDxUFfj6ZkN3Ye-xE5o5NpaArXYBY.png?width=320&crop=smart&auto=webp&s=dd062c35e057dbeda44694dc44283c31b6227696', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/PAkB__Sc2858_7NDxUFfj6ZkN3Ye-xE5o5NpaArXYBY.png?width=640&crop=smart&auto=webp&s=47dfe779dc83da89090b7cb95de618379850ccad', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/PAkB__Sc2858_7NDxUFfj6ZkN3Ye-xE5o5NpaArXYBY.png?width=960&crop=smart&auto=webp&s=d20511472a7e49af417ffdb32fb981ec9df341d3', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/PAkB__Sc2858_7NDxUFfj6ZkN3Ye-xE5o5NpaArXYBY.png?width=1080&crop=smart&auto=webp&s=5394218a7c16eee26b808c576b285ece83e0339d', 'width': 1080}], 'source': {'height': 640, 'url': 'https://external-preview.redd.it/PAkB__Sc2858_7NDxUFfj6ZkN3Ye-xE5o5NpaArXYBY.png?auto=webp&s=320ee3073e4555df6675f5f906c083975dfe5044', 'width': 1280}, 'variants': {}}]} |
DGX Spark LLM Fine-Tuning Performance | 6 | Unsloth published a [notebook](https://github.com/unslothai/notebooks/blob/main/nb/gpt_oss_(20B)_Reinforcement_Learning_2048_Game_DGX_Spark.ipynb) for LoRA fine-tuning of `gpt-oss-20b` with RL on a DGX Spark.
In the saved output, we can see that 1000 steps would take 88 hours, with `lora_rank = 4`, `batch_size = 2` and an (admittedly low) `max_seq_length = 768 `tokens.
11 steps / hour doesn't seem too shabby, and this will likely scale well to higher batch sizes like 32, enabled by the large memory on DGX Spark.
On a side note, I feel like people are focusing on DGX Spark as a personal inference machine, and unfortunately, that's not what it is.
DGX Spark is more akin to a desktop designed for researchers / devs, allowing research and development with the CUDA stack, where upon completion, software can be easily deployed to Nvidia's cloud offerings like the GB200. | 2025-10-14T20:48:35 | https://www.reddit.com/r/LocalLLaMA/comments/1o6rbmu/dgx_spark_llm_finetuning_performance/ | Mysterious_Finish543 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o6rbmu | false | null | t3_1o6rbmu | /r/LocalLLaMA/comments/1o6rbmu/dgx_spark_llm_finetuning_performance/ | false | false | self | 6 | {'enabled': False, 'images': [{'id': 'A3CJaaRV5I5tzhIdkkrkNwc0eR3HuHywZJvgzDmsQEM', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/A3CJaaRV5I5tzhIdkkrkNwc0eR3HuHywZJvgzDmsQEM.png?width=108&crop=smart&auto=webp&s=b2cd7ccfb3689af6615ce56fce14bf87236c8d6f', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/A3CJaaRV5I5tzhIdkkrkNwc0eR3HuHywZJvgzDmsQEM.png?width=216&crop=smart&auto=webp&s=948b6f22212b94b0e3ebd67e9fe54bb9e712b76c', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/A3CJaaRV5I5tzhIdkkrkNwc0eR3HuHywZJvgzDmsQEM.png?width=320&crop=smart&auto=webp&s=a92acdf4bbbae8ba79b49aef6b13c022316f1e27', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/A3CJaaRV5I5tzhIdkkrkNwc0eR3HuHywZJvgzDmsQEM.png?width=640&crop=smart&auto=webp&s=4fe9725f58230d7953668cba6e0cf3f1c7628955', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/A3CJaaRV5I5tzhIdkkrkNwc0eR3HuHywZJvgzDmsQEM.png?width=960&crop=smart&auto=webp&s=a436bfbb9f15337eae7e62263d81536f93764d52', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/A3CJaaRV5I5tzhIdkkrkNwc0eR3HuHywZJvgzDmsQEM.png?width=1080&crop=smart&auto=webp&s=eda93d80d9de2a9b04ad1648b06d605c02098aca', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/A3CJaaRV5I5tzhIdkkrkNwc0eR3HuHywZJvgzDmsQEM.png?auto=webp&s=5739fb8d587a7dd3261890c5d53a9c1d89512d36', 'width': 1200}, 'variants': {}}]} |
Real-time study buddy that sees your screen and talks back | 145 | Built a real-time learning assistant that sees your screen, talks, and learns alongside you. All open models (Qwen3-VL, Parakeet, Orpheus) wired together.
I shared a biology site on cell structure to see if it could describe the page, identify the diagram, and answer targeted questions about the mitochondria.
These text and vision models are getting so good. Wiring them together levels them all up. Next step: going to try running it across multiple sites and have it auto-summarize my learnings into a study guide or PDF after. | 2025-10-14T19:46:04 | https://v.redd.it/ctp0k9a3n4vf1 | Weary-Wing-6806 | /r/LocalLLaMA/comments/1o6pmxt/realtime_study_buddy_that_sees_your_screen_and/ | 1970-01-01T00:00:00 | 0 | {} | 1o6pmxt | false | {'reddit_video': {'bitrate_kbps': 5000, 'dash_url': 'https://v.redd.it/ctp0k9a3n4vf1/DASHPlaylist.mpd?a=1763192771%2CNTg3YzkxZDI2OThhOTMyM2YwZDRlMGEwNDRkZmEyMjkwNThmODRhZDE2Nzk0ZjMwNjk2ZGUzYjZlNGQyYWM3NQ%3D%3D&v=1&f=sd', 'duration': 130, 'fallback_url': 'https://v.redd.it/ctp0k9a3n4vf1/DASH_1080.mp4?source=fallback', 'has_audio': True, 'height': 1080, 'hls_url': 'https://v.redd.it/ctp0k9a3n4vf1/HLSPlaylist.m3u8?a=1763192771%2COTk4MGQ0ZjU3MzhkNjE5Y2RiOGFjZmIzNjdiNjdlOGIwZGY0NWNlMWMzY2VkNGJlMGE4YmZkODc1ZDMzYTA2YQ%3D%3D&v=1&f=sd', 'is_gif': False, 'scrubber_media_url': 'https://v.redd.it/ctp0k9a3n4vf1/DASH_96.mp4', 'transcoding_status': 'completed', 'width': 1920}} | t3_1o6pmxt | /r/LocalLLaMA/comments/1o6pmxt/realtime_study_buddy_that_sees_your_screen_and/ | false | false | 145 | {'enabled': False, 'images': [{'id': 'ZXlwZW5pYTNuNHZmMUsYDHUptOq0sYO1cNNkCl_tbC9KzkSKWyT6VTZxxWFL', 'resolutions': [{'height': 60, 'url': 'https://external-preview.redd.it/ZXlwZW5pYTNuNHZmMUsYDHUptOq0sYO1cNNkCl_tbC9KzkSKWyT6VTZxxWFL.png?width=108&crop=smart&format=pjpg&auto=webp&s=e703c05c9129bd6506a5e2460a1f3b8334f83d12', 'width': 108}, {'height': 121, 'url': 'https://external-preview.redd.it/ZXlwZW5pYTNuNHZmMUsYDHUptOq0sYO1cNNkCl_tbC9KzkSKWyT6VTZxxWFL.png?width=216&crop=smart&format=pjpg&auto=webp&s=2d9626d1f0bbb20cb8b4f9d4b397ad9e72ec74d9', 'width': 216}, {'height': 180, 'url': 'https://external-preview.redd.it/ZXlwZW5pYTNuNHZmMUsYDHUptOq0sYO1cNNkCl_tbC9KzkSKWyT6VTZxxWFL.png?width=320&crop=smart&format=pjpg&auto=webp&s=47d61f34ba694bf58b287ea22860ec66aa5f8062', 'width': 320}, {'height': 360, 'url': 'https://external-preview.redd.it/ZXlwZW5pYTNuNHZmMUsYDHUptOq0sYO1cNNkCl_tbC9KzkSKWyT6VTZxxWFL.png?width=640&crop=smart&format=pjpg&auto=webp&s=0a5f117c7222ee30e58ceb338f3a3cbc52c65f21', 'width': 640}, {'height': 540, 'url': 'https://external-preview.redd.it/ZXlwZW5pYTNuNHZmMUsYDHUptOq0sYO1cNNkCl_tbC9KzkSKWyT6VTZxxWFL.png?width=960&crop=smart&format=pjpg&auto=webp&s=7675d17a3c6d4e8a9ba7c9d3f2c0cc0172e10828', 'width': 960}, {'height': 607, 'url': 'https://external-preview.redd.it/ZXlwZW5pYTNuNHZmMUsYDHUptOq0sYO1cNNkCl_tbC9KzkSKWyT6VTZxxWFL.png?width=1080&crop=smart&format=pjpg&auto=webp&s=8a8e905775d58bd842e047a843171b24f89fbe8a', 'width': 1080}], 'source': {'height': 1080, 'url': 'https://external-preview.redd.it/ZXlwZW5pYTNuNHZmMUsYDHUptOq0sYO1cNNkCl_tbC9KzkSKWyT6VTZxxWFL.png?format=pjpg&auto=webp&s=2342907b59c4d966d5fb2c072f9ef8f5f333d443', 'width': 1920}, 'variants': {}}]} | |
Best path for a unified Gaming, AI & Server machine? Custom build vs. Mac Studio/DGX Spark | 2 | Hey everyone,
I'm trying to plan a single machine to handle everything—gaming, local LLMs, and home server duties.
I love the plug-and-play idea of a Mac Studio or DGX Spark, but I keep seeing benchmarks here that blow them away for less money, and just general negativity towards the Spark in particular.
So, am I crazy for even considering those pre-built options? For those of you running multi-GPU setups:
How much of a pain is it to build? Are there issues in having a single machine handle both AI and gaming? (For the record, I'm not a huge gamer, but would like to have access to a machine that lets me game)
What are the hidden headaches (power, cooling, motherboard issues) that I should be aware of? Is procuring a GPU still a pain, will I have a go through ebay to get something not outrageously overpriced?
Is the unified memory on a Mac Studio a big enough deal to compete with the raw power of multiple dedicated GPUs?
Just trying to figure out the best path forward without breaking the bank or creating a massive headache for myself. Any thoughts would be appreciated! | 2025-10-14T19:45:04 | https://www.reddit.com/r/LocalLLaMA/comments/1o6plzt/best_path_for_a_unified_gaming_ai_server_machine/ | valtor2 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o6plzt | false | null | t3_1o6plzt | /r/LocalLLaMA/comments/1o6plzt/best_path_for_a_unified_gaming_ai_server_machine/ | false | false | self | 2 | null |
Has anyone here used MLXLMCommon in Swift? What would error "noModelFactoryAvailable" mean when loading a model? | 1 | [removed] | 2025-10-14T19:14:46 | https://www.reddit.com/r/LocalLLaMA/comments/1o6ossf/has_anyone_here_used_mlxlmcommon_in_swift_what/ | busymom0 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o6ossf | false | null | t3_1o6ossf | /r/LocalLLaMA/comments/1o6ossf/has_anyone_here_used_mlxlmcommon_in_swift_what/ | false | false | self | 1 | null |
Is anyone considering the DGX Spark | 0 | I got in line to reserve one a few months back, and as of this morning they can be ordered. Should I make the jump? Haven't been keeping up with developments over the last few months so I'm not sure how it stacks up. | 2025-10-14T19:08:17 | https://www.reddit.com/r/LocalLLaMA/comments/1o6omjf/is_anyone_considering_the_dgx_spark/ | Commercial-West3390 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o6omjf | false | null | t3_1o6omjf | /r/LocalLLaMA/comments/1o6omjf/is_anyone_considering_the_dgx_spark/ | false | false | self | 0 | null |
Intel Crescent Island GPU: 160GB of LPDDR5X memory | 142 | **About the GPU:** The new data center GPU code-named Crescent Island is being designed to be power and cost-optimized for air-cooled enterprise servers and to incorporate large amounts of memory capacity and bandwidth, optimized for inference workflows.
Key features include:
* Xe3P microarchitecture with optimized performance-per-watt
* 160GB of LPDDR5X memory
* Support for a broad range of data types, ideal for “tokens-as-a-service” providers and inference use cases
[https://videocardz.com/newz/intel-confirms-xe3p-architecture-to-power-new-crescent-island-data-center-gpu-with-160gb-lpddr5x-memory](https://videocardz.com/newz/intel-confirms-xe3p-architecture-to-power-new-crescent-island-data-center-gpu-with-160gb-lpddr5x-memory)
[https://newsroom.intel.com/artificial-intelligence/intel-to-expand-ai-accelerator-portfolio-with-new-gpu](https://newsroom.intel.com/artificial-intelligence/intel-to-expand-ai-accelerator-portfolio-with-new-gpu)
| 2025-10-14T19:01:14 | https://www.reddit.com/r/LocalLLaMA/comments/1o6ofr9/intel_crescent_island_gpu_160gb_of_lpddr5x_memory/ | On1ineAxeL | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o6ofr9 | false | null | t3_1o6ofr9 | /r/LocalLLaMA/comments/1o6ofr9/intel_crescent_island_gpu_160gb_of_lpddr5x_memory/ | false | false | self | 142 | {'enabled': False, 'images': [{'id': 'R769YrweFNRwdXThG52Uz13EQll16NkxKsGPf_0Qi2c', 'resolutions': [{'height': 56, 'url': 'https://external-preview.redd.it/R769YrweFNRwdXThG52Uz13EQll16NkxKsGPf_0Qi2c.jpeg?width=108&crop=smart&auto=webp&s=15b53d29fd46a9c8aecd50e01de1a679fea1c98c', 'width': 108}, {'height': 112, 'url': 'https://external-preview.redd.it/R769YrweFNRwdXThG52Uz13EQll16NkxKsGPf_0Qi2c.jpeg?width=216&crop=smart&auto=webp&s=647b19ef7a2ebda960d97785df1df5bb066d7d4a', 'width': 216}, {'height': 166, 'url': 'https://external-preview.redd.it/R769YrweFNRwdXThG52Uz13EQll16NkxKsGPf_0Qi2c.jpeg?width=320&crop=smart&auto=webp&s=32c2ccedf164b2e52acc3a225693dcf77e2f0c1b', 'width': 320}, {'height': 332, 'url': 'https://external-preview.redd.it/R769YrweFNRwdXThG52Uz13EQll16NkxKsGPf_0Qi2c.jpeg?width=640&crop=smart&auto=webp&s=270c8a04eb08ac902f5f85e4fdced05522e20a1c', 'width': 640}, {'height': 499, 'url': 'https://external-preview.redd.it/R769YrweFNRwdXThG52Uz13EQll16NkxKsGPf_0Qi2c.jpeg?width=960&crop=smart&auto=webp&s=90165cbc013a023be64a7fc355ec9a44b4d42a4d', 'width': 960}, {'height': 561, 'url': 'https://external-preview.redd.it/R769YrweFNRwdXThG52Uz13EQll16NkxKsGPf_0Qi2c.jpeg?width=1080&crop=smart&auto=webp&s=39e2d6f7c07f4d7dfdd5c58d24413585bb23ec6f', 'width': 1080}], 'source': {'height': 1040, 'url': 'https://external-preview.redd.it/R769YrweFNRwdXThG52Uz13EQll16NkxKsGPf_0Qi2c.jpeg?auto=webp&s=1105008ed5276baf955e38ad5aa1a631d86b85d6', 'width': 2000}, 'variants': {}}]} |
If it's not local, it's not yours. | 1,148 | 2025-10-14T18:57:54 | inkberk | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1o6ocfs | false | null | t3_1o6ocfs | /r/LocalLLaMA/comments/1o6ocfs/if_its_not_local_its_not_yours/ | false | false | 1,148 | {'enabled': True, 'images': [{'id': 'SEgcKfsCidYMS89-8-i5SUB-lkwmERbCYmWBN89PVQM', 'resolutions': [{'height': 171, 'url': 'https://preview.redd.it/zzv4ey22j4vf1.png?width=108&crop=smart&auto=webp&s=8790a3bba1c01990adf5b1c305615cc6398a562c', 'width': 108}, {'height': 343, 'url': 'https://preview.redd.it/zzv4ey22j4vf1.png?width=216&crop=smart&auto=webp&s=7d9ba32fe0e986c8396ed423d39e1bd733d1fdc4', 'width': 216}, {'height': 508, 'url': 'https://preview.redd.it/zzv4ey22j4vf1.png?width=320&crop=smart&auto=webp&s=3806cb9b2ff44fb184cbc669e84260b5d34e6ad5', 'width': 320}, {'height': 1017, 'url': 'https://preview.redd.it/zzv4ey22j4vf1.png?width=640&crop=smart&auto=webp&s=ebc1f207746b0fa04e90a129bafad3aef0ca9971', 'width': 640}], 'source': {'height': 1510, 'url': 'https://preview.redd.it/zzv4ey22j4vf1.png?auto=webp&s=905e03287619f35a48ee4d992407500ebf04e226', 'width': 950}, 'variants': {}}]} | |||
Those who reserved Nvidia's DGX Spark are starting to receive purchase invitation emails | 38 | I just received this email | 2025-10-14T18:55:40 | sketharapu | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1o6oaa4 | false | null | t3_1o6oaa4 | /r/LocalLLaMA/comments/1o6oaa4/those_who_reserved_nvidias_dgx_spark_are_starting/ | false | false | default | 38 | {'enabled': True, 'images': [{'id': '7w1yhhrhj4vf1', 'resolutions': [{'height': 123, 'url': 'https://preview.redd.it/7w1yhhrhj4vf1.png?width=108&crop=smart&auto=webp&s=059e6bfdf0e0cd4748627c4fdd4c03ed9a78223e', 'width': 108}, {'height': 247, 'url': 'https://preview.redd.it/7w1yhhrhj4vf1.png?width=216&crop=smart&auto=webp&s=e31d34601fddbc56a321c113423da0ba11dc4d14', 'width': 216}, {'height': 367, 'url': 'https://preview.redd.it/7w1yhhrhj4vf1.png?width=320&crop=smart&auto=webp&s=91eeac77e7d2142fcf1b5105a12a5d2d17d3f3cf', 'width': 320}, {'height': 734, 'url': 'https://preview.redd.it/7w1yhhrhj4vf1.png?width=640&crop=smart&auto=webp&s=31ba63d90457b18277246650e0e8756589cac761', 'width': 640}, {'height': 1102, 'url': 'https://preview.redd.it/7w1yhhrhj4vf1.png?width=960&crop=smart&auto=webp&s=fc123f59aeb58f8d6eedb24fa7ce5f64a52c85b1', 'width': 960}, {'height': 1239, 'url': 'https://preview.redd.it/7w1yhhrhj4vf1.png?width=1080&crop=smart&auto=webp&s=ddaec78023648de9ca3ddb40f6e5277d3cab84cc', 'width': 1080}], 'source': {'height': 1458, 'url': 'https://preview.redd.it/7w1yhhrhj4vf1.png?auto=webp&s=e72c89e54322a78eddcfaf71e2cfd39bd6ef6f13', 'width': 1270}, 'variants': {}}]} | |
Nvidia DGX Spark | 0 | Looking for recommendation on where to order from. | 2025-10-14T18:48:38 | https://www.reddit.com/r/LocalLLaMA/comments/1o6o3mr/nvidia_dgx_spark/ | Psychological_Ad8426 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o6o3mr | false | null | t3_1o6o3mr | /r/LocalLLaMA/comments/1o6o3mr/nvidia_dgx_spark/ | false | false | self | 0 | null |
Qwen3-VL-4B and 8B Instruct & Thinking are here | 156 | [https://huggingface.co/Qwen/Qwen3-VL-4B-Thinking](https://huggingface.co/Qwen/Qwen3-VL-4B-Thinking)
[https://huggingface.co/Qwen/Qwen3-VL-8B-Thinking](https://huggingface.co/Qwen/Qwen3-VL-8B-Thinking)
[https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct)
[https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct)
You can already run Qwen3-VL-4B & 8B locally Day-0 on NPU/GPU/CPU using MLX, GGUF, and NexaML with NexaSDK **(**[**GitHub**](https://github.com/NexaAI/nexa-sdk)**)**
Check out GGUF, MLX, and NexaML collection on HuggingFace: [https://huggingface.co/collections/NexaAI/qwen3vl-68d46de18fdc753a7295190a](https://huggingface.co/collections/NexaAI/qwen3vl-68d46de18fdc753a7295190a) | 2025-10-14T18:08:00 | https://www.reddit.com/r/LocalLLaMA/comments/1o6n0tm/qwen3vl4b_and_8b_instruct_thinking_are_here/ | AlanzhuLy | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o6n0tm | true | null | t3_1o6n0tm | /r/LocalLLaMA/comments/1o6n0tm/qwen3vl4b_and_8b_instruct_thinking_are_here/ | false | false | self | 156 | {'enabled': False, 'images': [{'id': 'HkAHIHQiRPWIYni7IxAZZBjLOtl0MrWGfOxubxqm8vw', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/HkAHIHQiRPWIYni7IxAZZBjLOtl0MrWGfOxubxqm8vw.png?width=108&crop=smart&auto=webp&s=68aeaa615c31d89f711e0e3de13a2282af2ba731', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/HkAHIHQiRPWIYni7IxAZZBjLOtl0MrWGfOxubxqm8vw.png?width=216&crop=smart&auto=webp&s=159f5c9e9b7367a84798ebef6df4a44832d67412', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/HkAHIHQiRPWIYni7IxAZZBjLOtl0MrWGfOxubxqm8vw.png?width=320&crop=smart&auto=webp&s=16bac46433bb06570157d6fb30bc4b0875a79a1e', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/HkAHIHQiRPWIYni7IxAZZBjLOtl0MrWGfOxubxqm8vw.png?width=640&crop=smart&auto=webp&s=dfad40fa75d4e9842d2e17a247356b3dbc082fa1', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/HkAHIHQiRPWIYni7IxAZZBjLOtl0MrWGfOxubxqm8vw.png?width=960&crop=smart&auto=webp&s=fc3be0b8fbb8bae2881aab8f79f28fbfc0ddd601', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/HkAHIHQiRPWIYni7IxAZZBjLOtl0MrWGfOxubxqm8vw.png?width=1080&crop=smart&auto=webp&s=c1efa7833cea701500efe98b215bbb915f196647', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/HkAHIHQiRPWIYni7IxAZZBjLOtl0MrWGfOxubxqm8vw.png?auto=webp&s=b37c529fdc0a37dc31680185ecf7a21ecbbd4348', 'width': 1200}, 'variants': {}}]} |
Qwen3-VL-4B and 8B GGUF, MLX, NexaML Day-0 Support | 8 | You can already run Qwen3-VL-4B & 8B locally Day-0 on NPU/GPU/CPU using MLX, GGUF, and NexaML with NexaSDK.
Our team didn't sleep last night. Every line of model inference code in NexaML, GGML, and MLX was built from scratch by Nexa for SOTA performance on each hardware stack, powered by Nexa’s unified inference engine. How we did it: [https://nexa.ai/blogs/qwen3vl](https://nexa.ai/blogs/qwen3vl)
# How to get started:
**Step 1. Install NexaSDK (**[**GitHub**](https://github.com/NexaAI/nexa-sdk)**)**
**Step 2. Run in your terminal with one line of code**
CPU/GPU for everyone (GGML):
`nexa infer NexaAI/Qwen3-VL-4B-Thinking-GGUF`
`nexa infer NexaAI/Qwen3-VL-8B-Instruct-GGUF`
Apple Silicon (MLX):
`nexa infer nexa infer NexaAI/Qwen3-VL-4B-MLX-4bit`
`nexa infer NexaAI/qwen3vl-8B-Thinking-4bit-mlx`
Qualcomm NPU (NexaML):
`nexa infer NexaAI/Qwen3-VL-4B-Instruct-NPU`
`nexa infer NexaAI/Qwen3-VL-4B-Thinking-NPU`
Check out our GGUF, MLX, and NexaML collection on HuggingFace: [https://huggingface.co/collections/NexaAI/qwen3vl-68d46de18fdc753a7295190a](https://huggingface.co/collections/NexaAI/qwen3vl-68d46de18fdc753a7295190a)
If this helps, give us a ⭐ on [GitHub](https://github.com/NexaAI/nexa-sdk) — we’d love to hear feedback or benchmarks from your setup. Curious what you’ll build with multimodal Qwen3-VL running natively on your machine. | 2025-10-14T17:59:38 | https://www.reddit.com/r/LocalLLaMA/comments/1o6ms8j/qwen3vl4b_and_8b_gguf_mlx_nexaml_day0_support/ | AlanzhuLy | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o6ms8j | false | null | t3_1o6ms8j | /r/LocalLLaMA/comments/1o6ms8j/qwen3vl4b_and_8b_gguf_mlx_nexaml_day0_support/ | false | false | self | 8 | null |
Trouble running Qwen3-30b-a3b VL. “error loading model architecture: unknown model architecture: qwen3vlmoe” | 2 | As the title states. Have tried running the q8_0 gguf from huihui-ai on ollama and llama.cpp directly with no luck. Anyone have any tips? I’m a newcomer here. | 2025-10-14T17:32:02 | https://www.reddit.com/r/LocalLLaMA/comments/1o6m08t/trouble_running_qwen330ba3b_vl_error_loading/ | ElectronicBend6984 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o6m08t | false | null | t3_1o6m08t | /r/LocalLLaMA/comments/1o6m08t/trouble_running_qwen330ba3b_vl_error_loading/ | false | false | self | 2 | null |
What are the best local LLM models for a single text classification task? | 1 | I have a single task: I have thousands of news headlines and I simply need to identify whether or not a headline is political in nature or not.
I got pretty good success using Apple Intelligence APIs in Swift running on an M4 Mac mini. However, some headlines trigger the Apple Intelligence's guardrails. So, I am looking at alternatives.
What are the best local models I can run for this task? | 2025-10-14T17:24:09 | https://www.reddit.com/r/LocalLLaMA/comments/1o6lsbv/what_are_the_best_local_llm_models_for_a_single/ | busymom0 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o6lsbv | false | null | t3_1o6lsbv | /r/LocalLLaMA/comments/1o6lsbv/what_are_the_best_local_llm_models_for_a_single/ | false | false | self | 1 | null |
Looking for testers for Isuite-TTS, a lightweight offline Text-to-Speech library – Feedback welcome! | 1 | [removed] | 2025-10-14T17:12:39 | https://www.reddit.com/r/LocalLLaMA/comments/1o6lgx9/looking_for_testers_for_isuitetts_a_lightweight/ | isuite-dev | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o6lgx9 | false | null | t3_1o6lgx9 | /r/LocalLLaMA/comments/1o6lgx9/looking_for_testers_for_isuitetts_a_lightweight/ | false | false | self | 1 | {'enabled': False, 'images': [{'id': 'Q4PyNKwrHA9yWkZ_v07piqz_3WRgvam8mXcFvWb1OeM', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/Q4PyNKwrHA9yWkZ_v07piqz_3WRgvam8mXcFvWb1OeM.png?width=108&crop=smart&auto=webp&s=f1c5bf1a2dc6d8fcbcc300b713185600228347d8', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/Q4PyNKwrHA9yWkZ_v07piqz_3WRgvam8mXcFvWb1OeM.png?width=216&crop=smart&auto=webp&s=043b4e4f6f6b051044e7a6b550c66907d60fba2f', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/Q4PyNKwrHA9yWkZ_v07piqz_3WRgvam8mXcFvWb1OeM.png?width=320&crop=smart&auto=webp&s=04892d9b9c6f8bd2508d735c05da46d8542a2ed5', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/Q4PyNKwrHA9yWkZ_v07piqz_3WRgvam8mXcFvWb1OeM.png?width=640&crop=smart&auto=webp&s=d7403c2eb360081f11f0fdbf7ac183ababad1d72', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/Q4PyNKwrHA9yWkZ_v07piqz_3WRgvam8mXcFvWb1OeM.png?width=960&crop=smart&auto=webp&s=bba74d58318ff9b1d6a23cbf419eb2051fe85e74', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/Q4PyNKwrHA9yWkZ_v07piqz_3WRgvam8mXcFvWb1OeM.png?width=1080&crop=smart&auto=webp&s=1eb8f9c6b76d13e9a4530f41f5bbb9ee388423a0', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/Q4PyNKwrHA9yWkZ_v07piqz_3WRgvam8mXcFvWb1OeM.png?auto=webp&s=1efc768f3551714f6936c8da5addcce32b047197', 'width': 1200}, 'variants': {}}]} |
KAT-Dev-72B-Exp I tried from the community a couple of days ago: high scores don’t mean it wins everywhere | 16 | Credit where it’s due: what first caught my eye was its 74.6% on SWE-Bench Verified among open-source models (evaluated with the SWE-agent scaffold) , pretty encouraging. But in the engineering world, “benchmarks = reality” rarely holds. Cross-repo coupling, legacy landmines, and CI magic can all throw a model off rhythm. I care more about “steady-state performance” in real repos: first-pass success rate, average time-to-fix, rollback rate, these numbers guide team decisions better than a single score.
The official messaging is candid too: KAT-Dev-72B-Exp is an experimental RL line of KAT-Coder to showcase RL innovations; the stronger KAT-Coder has a free trial on StreamLake, which basically gives everyone ready-made conditions for A/B testing. I recommend benchmarking on your own repo and workflow, not just staring at promo charts. RL can easily pick up “benchmark-friendly habits,” but in real repos with crusty scripts, cross-service changes, and quirky pipelines, my hands-on experience wasn’t as stellar as the benchmark results suggest.
Weights and docs: [https://huggingface.co/Kwaipilot/KAT-Dev-72B-Exp](https://huggingface.co/Kwaipilot/KAT-Dev-72B-Exp) | 2025-10-14T16:50:17 | https://www.reddit.com/r/LocalLLaMA/comments/1o6kuso/katdev72bexp_i_tried_from_the_community_a_couple/ | Hairy-Librarian3796 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o6kuso | false | null | t3_1o6kuso | /r/LocalLLaMA/comments/1o6kuso/katdev72bexp_i_tried_from_the_community_a_couple/ | false | false | self | 16 | {'enabled': False, 'images': [{'id': 'rxyepxgYUof3_-pxPA16Sj6OoiuoO3OTQiZrKV-cxps', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/rxyepxgYUof3_-pxPA16Sj6OoiuoO3OTQiZrKV-cxps.png?width=108&crop=smart&auto=webp&s=cf18d5cf8ded0a10c6a0af997508a324c1a4598f', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/rxyepxgYUof3_-pxPA16Sj6OoiuoO3OTQiZrKV-cxps.png?width=216&crop=smart&auto=webp&s=ea48d1283c6607aaf89ea7f8ffb47f5bc99ce20d', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/rxyepxgYUof3_-pxPA16Sj6OoiuoO3OTQiZrKV-cxps.png?width=320&crop=smart&auto=webp&s=cba31dfcbe87d2c0403c3a3c70b8bfe95eb1e2d1', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/rxyepxgYUof3_-pxPA16Sj6OoiuoO3OTQiZrKV-cxps.png?width=640&crop=smart&auto=webp&s=6a34ebb6475d3fb833395063cb58949ed7cc21cd', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/rxyepxgYUof3_-pxPA16Sj6OoiuoO3OTQiZrKV-cxps.png?width=960&crop=smart&auto=webp&s=4d6cc5c98d3e061ccdb88c37a77110422f322645', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/rxyepxgYUof3_-pxPA16Sj6OoiuoO3OTQiZrKV-cxps.png?width=1080&crop=smart&auto=webp&s=ab0d2724e27fd5da8661dce70df2d4d765794815', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/rxyepxgYUof3_-pxPA16Sj6OoiuoO3OTQiZrKV-cxps.png?auto=webp&s=d56a39ad375ea48de4663c2472edcdff1c7fc561', 'width': 1200}, 'variants': {}}]} |
Qwen3-VL-4B and 8B Instruct & Thinking are here | 324 | [https://huggingface.co/Qwen/Qwen3-VL-4B-Thinking](https://huggingface.co/Qwen/Qwen3-VL-4B-Thinking)
[https://huggingface.co/Qwen/Qwen3-VL-8B-Thinking](https://huggingface.co/Qwen/Qwen3-VL-8B-Thinking)
[https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct)
[https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct) | 2025-10-14T16:31:24 | https://www.reddit.com/r/LocalLLaMA/comments/1o6kchz/qwen3vl4b_and_8b_instruct_thinking_are_here/ | AlanzhuLy | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o6kchz | false | null | t3_1o6kchz | /r/LocalLLaMA/comments/1o6kchz/qwen3vl4b_and_8b_instruct_thinking_are_here/ | false | false | self | 324 | {'enabled': False, 'images': [{'id': 'HkAHIHQiRPWIYni7IxAZZBjLOtl0MrWGfOxubxqm8vw', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/HkAHIHQiRPWIYni7IxAZZBjLOtl0MrWGfOxubxqm8vw.png?width=108&crop=smart&auto=webp&s=68aeaa615c31d89f711e0e3de13a2282af2ba731', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/HkAHIHQiRPWIYni7IxAZZBjLOtl0MrWGfOxubxqm8vw.png?width=216&crop=smart&auto=webp&s=159f5c9e9b7367a84798ebef6df4a44832d67412', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/HkAHIHQiRPWIYni7IxAZZBjLOtl0MrWGfOxubxqm8vw.png?width=320&crop=smart&auto=webp&s=16bac46433bb06570157d6fb30bc4b0875a79a1e', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/HkAHIHQiRPWIYni7IxAZZBjLOtl0MrWGfOxubxqm8vw.png?width=640&crop=smart&auto=webp&s=dfad40fa75d4e9842d2e17a247356b3dbc082fa1', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/HkAHIHQiRPWIYni7IxAZZBjLOtl0MrWGfOxubxqm8vw.png?width=960&crop=smart&auto=webp&s=fc3be0b8fbb8bae2881aab8f79f28fbfc0ddd601', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/HkAHIHQiRPWIYni7IxAZZBjLOtl0MrWGfOxubxqm8vw.png?width=1080&crop=smart&auto=webp&s=c1efa7833cea701500efe98b215bbb915f196647', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/HkAHIHQiRPWIYni7IxAZZBjLOtl0MrWGfOxubxqm8vw.png?auto=webp&s=b37c529fdc0a37dc31680185ecf7a21ecbbd4348', 'width': 1200}, 'variants': {}}]} |
[Open Source] Introducing a new AI framework | 0 | Hey [r/LocalLLaMA](https://www.reddit.com/r/LocalLLaMA/) ! 👋
If you are looking for:
* a Multimodal RAGs
* a Multitenant Chatbots
* agentic tool orchestration, even via MCP clients
* something easily extendable via plugins
* something enterprise ready
all in one framework, then please give a change to my [CheshireCat](https://www.github.com/matteocacciola/cheshirecat-core)
# Why use the Cheshire Cat?
[](https://github.com/matteocacciola/cheshirecat-core?tab=readme-ov-file#why-use-the-cheshire-cat)
The Cheshire Cat is a framework to build custom AI agents:
* 🤖 Build your own AI agent in minutes, not months
* 🧠 Make it smart with Retrieval Augmented Generation (RAG)
* 🏆 Multi-modality, to build the RAG with any kind of documents
* 💬 Multi-tenancy, to manage multiple chatbots at the same time, each with its own settings, plugins, LLMs, etc.
* ⚡️ API first, to easily add a conversational layer to your app
* ☁️ Cloud Ready, working even with horizontal autoscaling
* 🔐 Secure by design, with API Key and granular permissions
* 🏗 Production ready, cloud native and scalable
* 🐋 100% dockerized, to run anywhere
* 🛠 Easily extendable with plugins
* 🧩 Built-in plugins
* 🪛 Extend core components (file managers, LLMs, vector databases)
* ✂️ Customizable chunking and embedding
* 🛠 Custom tools, forms, endpoints, MCP clients
* 🪛 LLM callbacks
* 🌐 Customizable integration of **MCP clients**, such as LangSmith or LlamaIndex
* 🏛 Easy to use Admin Panel (available with the repository [matteocacciola/cheshirecat-admin](https://www.github.com/matteocacciola/cheshirecat-admin))
* 🦄 Easy to understand [docs](https://deepwiki.com/matteocacciola/cheshirecat-core)
* 🌍 Supports any language model via LangChain
**Star the project on GitHub if you find this interesting,** it genuinely helps me understanding if I am solving real problems.
Happy to answer any questions in the comments! | 2025-10-14T16:27:24 | https://www.reddit.com/r/LocalLLaMA/comments/1o6k8mk/open_source_introducing_a_new_ai_framework/ | Fit-Reach-1058 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1o6k8mk | false | null | t3_1o6k8mk | /r/LocalLLaMA/comments/1o6k8mk/open_source_introducing_a_new_ai_framework/ | false | false | self | 0 | null |
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