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Inference of LLMs with offloading to SSD(NVMe) | 20 | Hey folks 👋 Sorry for the long post, I added a TLDR at the end.
The company that I work at wants to see if it's possible (and somewhat usable) to use GPU+SSD(NVMe) offloading for models which far exceed the VRAM of a GPU.
I know llama cpp and ollama basically takes care of this by offloading to CPU, and it's slower than just GPU, but I want to see if I can use SSD offloading and get atleast 2-3 tk/s.
The model that I am interested to run is [llama3.3 70b BF16 quantization](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct/tree/main) (and hopefully other similar sized models), and I have an L40s with 48GB VRAM.
I was researching about this and came across something called [DeepSpeed](https://www.deepspeed.ai/), and I saw DeepNVMe and it's application in their Zero-Inference optimization.
They have three configs to use Zero-Inference as far as I understood, stage 1 is GPU, stage 2 CPU offload and stage 3 is NVMe, and I could not figure out how to use it with disk, so I first tried their CPU offload config.
Instead of offloading the model to RAM when the GPU's VRAM is full, it is simply throwing a CUDA OOM error. Then I tried to load the model entirely in RAM then offload to GPU, but I am unable to control how much to offload to GPU(I can see around 7 GB usage with nvidia-smi) so almost all of the model is in RAM.
The prompt I gave: Tell mahabharata in 100 words .
With ollama and their llama 3.3 70b (77 GB and 8-bit quantization), I was able to get 2.36 tk/s. I know mine is BF16, but the time it took to generate the same prompt was 831 seconds, around 14 minutes! DeepSpeed doesn't support GGUF format and I could not find an 8-bit quantization model for similar testing, but the result should not be this bad right?
The issue is most likely my bad config and script and lack of understanding of how this works, I am a total noob. But if anyone has any experience with DeepSpeed or offloading to disk for inference, provide your suggestions on how to tackle this, any other better ways if any, and whether it's feasible at all.
Run log: https://paste.laravel.io/ce6a36ef-1453-4788-84ac-9bc54b347733
TLDR: To save costs, I want to run or inference models by offloading to disk(NVMe). Tried DeepSpeed but couldn't make it work, would appreciate some suggestions and insights. | 2025-10-06T21:20:37 | GRIFFITHUUU | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1nzvtp9 | false | null | t3_1nzvtp9 | /r/LocalLLaMA/comments/1nzvtp9/inference_of_llms_with_offloading_to_ssdnvme/ | false | false | default | 20 | {'enabled': True, 'images': [{'id': 'xk0isd276ktf1', 'resolutions': [{'height': 50, 'url': 'https://preview.redd.it/xk0isd276ktf1.png?width=108&crop=smart&auto=webp&s=7f67995d1a0112e8d1210792c6d2198af7dca300', 'width': 108}, {'height': 101, 'url': 'https://preview.redd.it/xk0isd276ktf1.png?width=216&crop=smart&auto=webp&s=be05d8ec634b6ce80f63e30256bb26c85154347b', 'width': 216}, {'height': 149, 'url': 'https://preview.redd.it/xk0isd276ktf1.png?width=320&crop=smart&auto=webp&s=4b3bdc453b33c0f327df5ce86bfeee33c6a7e7f3', 'width': 320}, {'height': 299, 'url': 'https://preview.redd.it/xk0isd276ktf1.png?width=640&crop=smart&auto=webp&s=59b6fdd51e74bd09c9915da933595aac2065e0b6', 'width': 640}, {'height': 448, 'url': 'https://preview.redd.it/xk0isd276ktf1.png?width=960&crop=smart&auto=webp&s=57cda9495d70fa0f0f01007a973a965985ded6de', 'width': 960}, {'height': 505, 'url': 'https://preview.redd.it/xk0isd276ktf1.png?width=1080&crop=smart&auto=webp&s=63fa9dfc8efd7944b242f3e5f420693e1784e8af', 'width': 1080}], 'source': {'height': 505, 'url': 'https://preview.redd.it/xk0isd276ktf1.png?auto=webp&s=086ab5718d868e58ea10d18093b9941d15fce29f', 'width': 1080}, 'variants': {}}]} | |
How do I make DeepSeek 3.1... Think? In Msty Studio? | 0 | I'm quite new and inexperienced. I asked AI, but... frankly it doesn't know what it's talking about, lol. Or it's using old data or something. I'm not sure. | 2025-10-06T21:11:46 | https://www.reddit.com/r/LocalLLaMA/comments/1nzvl0y/how_do_i_make_deepseek_31_think_in_msty_studio/ | PangurBanTheCat | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzvl0y | false | null | t3_1nzvl0y | /r/LocalLLaMA/comments/1nzvl0y/how_do_i_make_deepseek_31_think_in_msty_studio/ | false | false | self | 0 | null |
You can start building and testing apps in ChatGPT with the Apps SDK preview, which we're releasing today as an open standard built on MCP. | 0 | 2025-10-06T21:09:33 | https://www.youtube.com/watch?v=2C4Cs6503gw | Late-Scarcity-5476 | youtube.com | 1970-01-01T00:00:00 | 0 | {} | 1nzvixu | false | null | t3_1nzvixu | /r/LocalLLaMA/comments/1nzvixu/you_can_start_building_and_testing_apps_in/ | false | false | default | 0 | null | |
Run Open AI GPT-OSS on a mobile phone (Demo) | 22 | Sam Altman recently said: “GPT-OSS has strong real-world performance comparable to o4-mini—and you can run it locally on your phone.” Many believed running a 20B-parameter model on mobile devices was still years away.
I am from [Nexa AI](https://github.com/NexaAI/nexa-sdk), we’ve managed to run GPT-OSS on a mobile phone for real and want to share with you a demo and its performance
GPT-OSS-20B on Snapdragon Gen 5 with ASUS ROG 9 phone
* 17 tokens/sec decoding speed
* < 3 seconds Time-to-First-Token
We think it is super cool and would love to hear everyone's thought. | 2025-10-06T21:08:24 | https://v.redd.it/o92q3wh03ktf1 | AlanzhuLy | v.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1nzvhth | false | {'reddit_video': {'bitrate_kbps': 5000, 'dash_url': 'https://v.redd.it/o92q3wh03ktf1/DASHPlaylist.mpd?a=1762376918%2CNGQ4MDhiNzEwMjA5ODU4ZWY0YjhhNjI4ZTU2ODk5NTlhNjJlYjU4YTc3YzRjMWVmYjIyZDJkYWJjMDk4ZjRkMQ%3D%3D&v=1&f=sd', 'duration': 31, 'fallback_url': 'https://v.redd.it/o92q3wh03ktf1/DASH_1080.mp4?source=fallback', 'has_audio': False, 'height': 1920, 'hls_url': 'https://v.redd.it/o92q3wh03ktf1/HLSPlaylist.m3u8?a=1762376918%2CODcxZmQ3NDAzNmJjNzE5YTYwMmRjMzdjZWM2YjFkMjk5MDc2YWJkYTcwZjNjYzM4OWM5ZmU3YjM5ZTQyZWM1OA%3D%3D&v=1&f=sd', 'is_gif': False, 'scrubber_media_url': 'https://v.redd.it/o92q3wh03ktf1/DASH_96.mp4', 'transcoding_status': 'completed', 'width': 996}} | t3_1nzvhth | /r/LocalLLaMA/comments/1nzvhth/run_open_ai_gptoss_on_a_mobile_phone_demo/ | false | false | 22 | {'enabled': False, 'images': [{'id': 'MGVqd2I4aDAza3RmMW_X92lPZSdh6fcDHohlh8eF1OAewrbD-P9SZJPkXXH-', 'resolutions': [{'height': 208, 'url': 'https://external-preview.redd.it/MGVqd2I4aDAza3RmMW_X92lPZSdh6fcDHohlh8eF1OAewrbD-P9SZJPkXXH-.png?width=108&crop=smart&format=pjpg&auto=webp&s=71b74f1520346936b7d468156bb991fb110f48da', 'width': 108}, {'height': 416, 'url': 'https://external-preview.redd.it/MGVqd2I4aDAza3RmMW_X92lPZSdh6fcDHohlh8eF1OAewrbD-P9SZJPkXXH-.png?width=216&crop=smart&format=pjpg&auto=webp&s=30dc2aac231bbf85571e858cf05053ffea85cc6a', 'width': 216}, {'height': 617, 'url': 'https://external-preview.redd.it/MGVqd2I4aDAza3RmMW_X92lPZSdh6fcDHohlh8eF1OAewrbD-P9SZJPkXXH-.png?width=320&crop=smart&format=pjpg&auto=webp&s=24044aa97c0166a98fff64081e82081403f76365', 'width': 320}, {'height': 1234, 'url': 'https://external-preview.redd.it/MGVqd2I4aDAza3RmMW_X92lPZSdh6fcDHohlh8eF1OAewrbD-P9SZJPkXXH-.png?width=640&crop=smart&format=pjpg&auto=webp&s=42fc0598b90f3bde87865e5051d37a443090d25b', 'width': 640}, {'height': 1851, 'url': 'https://external-preview.redd.it/MGVqd2I4aDAza3RmMW_X92lPZSdh6fcDHohlh8eF1OAewrbD-P9SZJPkXXH-.png?width=960&crop=smart&format=pjpg&auto=webp&s=4fc5b9702875cf235fe26968d2018c4d221752e0', 'width': 960}, {'height': 2082, 'url': 'https://external-preview.redd.it/MGVqd2I4aDAza3RmMW_X92lPZSdh6fcDHohlh8eF1OAewrbD-P9SZJPkXXH-.png?width=1080&crop=smart&format=pjpg&auto=webp&s=31409cf985701d18c36186ad51628e8410754ac0', 'width': 1080}], 'source': {'height': 2160, 'url': 'https://external-preview.redd.it/MGVqd2I4aDAza3RmMW_X92lPZSdh6fcDHohlh8eF1OAewrbD-P9SZJPkXXH-.png?format=pjpg&auto=webp&s=fd91db396da313ec9adc705f11add2c32f9ff299', 'width': 1120}, 'variants': {}}]} | |
How to make a PyTorch trained model behave "similarly" on WebGPU? | 2 | For an experiment of mine I was taking a pre-trained PyTorch model and tried to export it as ONNX and then run it with WebGPU. While I was able to make it run indeed, the output of the model turned out to be vastly different using WebGPU compared to running it (on same computer) with PyTorch. ChatGPT recommended I try to export the model with the --nms parameter set, that did not seem to improve things in any way.
Now I need to figure out what to do to make the model behave "same" (or at least sufficiently close) to the original PyTorch environment.
If anyone has any experience with that, any help would be appreciated. | 2025-10-06T20:55:03 | https://www.reddit.com/r/LocalLLaMA/comments/1nzv4gw/how_to_make_a_pytorch_trained_model_behave/ | fabkosta | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzv4gw | false | null | t3_1nzv4gw | /r/LocalLLaMA/comments/1nzv4gw/how_to_make_a_pytorch_trained_model_behave/ | false | false | self | 2 | null |
Best model for? | 0 | I have a project that basically it cleans web scraper data using scraper and selenium. Basically will look at a couple hundred companies build profiles mainly looking at competitive analysis. So a page scraper might pull a page on a company case study in a ton of different formats. I would want the llm to decern facts, like names of brands, technology and services and parse it. I have it working reasonably well on an OpenAi api but love to experiment.
PC specs, Asus Rog Laptop 4.2 ghz, 40 go ram, Nvidia 3060 processer. I can put some logic to offload more complex work to a cloud Api. But what model would be good for this? Using Docker. | 2025-10-06T20:47:00 | https://www.reddit.com/r/LocalLLaMA/comments/1nzuwrp/best_model_for/ | nofilmincamera | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzuwrp | false | null | t3_1nzuwrp | /r/LocalLLaMA/comments/1nzuwrp/best_model_for/ | false | false | self | 0 | null |
Adaptive + Codex → automatic GPT-5 model routing | 5 | We just released an integration for **OpenAI Codex** that removes the need to manually pick *Minimal / Low / Medium / High* GPT-5 levels.
Instead, Adaptive acts as a drop-in replacement for the Codex API and routes prompts automatically.
How it works:
→ The prompt is analyzed.
→ **Task complexity** \+ **domain** are detected.
→ That’s mapped to criteria for model selection.
→ A **semantic search** runs across GPT-5 models.
→ The request is routed to the best fit.
What this means in practice:
→ **Faster speed:** lightweight edits hit smaller GPT-5 models.
→ **Higher quality:** complex prompts are routed to larger GPT-5 models.
→ **Less friction:** no toggling reasoning levels inside Codex.
Setup guide: [https://docs.llmadaptive.uk/developer-tools/codex](https://docs.llmadaptive.uk/developer-tools/codex) | 2025-10-06T20:20:49 | https://www.reddit.com/r/LocalLLaMA/comments/1nzu6lw/adaptive_codex_automatic_gpt5_model_routing/ | botirkhaltaev | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzu6lw | false | null | t3_1nzu6lw | /r/LocalLLaMA/comments/1nzu6lw/adaptive_codex_automatic_gpt5_model_routing/ | false | false | self | 5 | {'enabled': False, 'images': [{'id': 'QvlunybrCu24ZDA_4HbnUCX_jDz-4cQkrtrBs-8cFoI', 'resolutions': [{'height': 56, 'url': 'https://external-preview.redd.it/QvlunybrCu24ZDA_4HbnUCX_jDz-4cQkrtrBs-8cFoI.png?width=108&crop=smart&auto=webp&s=343bf73eec3999a0522f69e54211600c5903125b', 'width': 108}, {'height': 113, 'url': 'https://external-preview.redd.it/QvlunybrCu24ZDA_4HbnUCX_jDz-4cQkrtrBs-8cFoI.png?width=216&crop=smart&auto=webp&s=e3a6e41c63cc9442bc63f500e443b8668288080e', 'width': 216}, {'height': 168, 'url': 'https://external-preview.redd.it/QvlunybrCu24ZDA_4HbnUCX_jDz-4cQkrtrBs-8cFoI.png?width=320&crop=smart&auto=webp&s=37d9ef5184994e3cb87cf519a796980ed256bfb0', 'width': 320}, {'height': 336, 'url': 'https://external-preview.redd.it/QvlunybrCu24ZDA_4HbnUCX_jDz-4cQkrtrBs-8cFoI.png?width=640&crop=smart&auto=webp&s=a18cbb2db6d6a62ec2b315ac5e9d5f9139b5123c', 'width': 640}, {'height': 504, 'url': 'https://external-preview.redd.it/QvlunybrCu24ZDA_4HbnUCX_jDz-4cQkrtrBs-8cFoI.png?width=960&crop=smart&auto=webp&s=f03456a2918a8e0dc238427ed71d9a80658a2eae', 'width': 960}, {'height': 567, 'url': 'https://external-preview.redd.it/QvlunybrCu24ZDA_4HbnUCX_jDz-4cQkrtrBs-8cFoI.png?width=1080&crop=smart&auto=webp&s=31135cc71bc0ff1d90a0f6ff2acc47a14627b87f', 'width': 1080}], 'source': {'height': 630, 'url': 'https://external-preview.redd.it/QvlunybrCu24ZDA_4HbnUCX_jDz-4cQkrtrBs-8cFoI.png?auto=webp&s=ee98cc44932fc4df1a1136a899d6cb62f0f5a319', 'width': 1200}, 'variants': {}}]} |
Calling AI Business Leaders and AI Engineers | 0 | I’m conducting research on Responsible AI Leadership and how industry leaders perceive their role in developing AI and robotics that do not fully displace human jobs.
If you’re an AI or robotics executive and/or AI engineers interested in sharing your insights through a 30-40 minute interview, please reach out! Your experience will help shape ethical innovation practices in AI.
This study has received ethical approval from the Research Ethics Board, University of Ottawa
Email [cintahch-research@uottawa.ca](mailto:cintahch-research@uottawa.ca) to participate or learn more.
Principal Investigator: Channarong Intahchomphoo Adjunct Professor, School of Engineering Design and Teaching Innovation Faculty of Engineering, University of Ottawa, Canada | 2025-10-06T20:08:17 | https://www.reddit.com/r/LocalLLaMA/comments/1nztu1x/calling_ai_business_leaders_and_ai_engineers/ | Queasy-Ad-6945 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nztu1x | false | null | t3_1nztu1x | /r/LocalLLaMA/comments/1nztu1x/calling_ai_business_leaders_and_ai_engineers/ | false | false | self | 0 | null |
LM Studio + Open Web UI | 2 | I'm trying to connect Open Web UI to LM Studio as I want to use the downloaded models via a web GUI. I've watched YT videos and even tried asking ChatGPT, and looking for similar posts here but I am unable to get past the configuration.
My setup is as follows:
Open Web UI - docker container on a Proxmox VM (Computer A)
LM Studio - on Windows Laptop (Computer B)
None of the YT videos I watched had this option OpeAPI Spec > openapi.json
https://preview.redd.it/nmjnzunjpjtf1.png?width=863&format=png&auto=webp&s=093c8096a76d40e474cab410d35f99a0b9f33af4
I know LM Studio works on the network because my n8n workflow on docker running on Computer A is able to fetch the models from LM Studio (Computer B).
Using the LM Studio URL `http://Computer_B_IP:1234/v1` seems to connect, but the logs shows the error `Unexpected endpoint or method. (GET /v1/openapi.json). Returning 200 anyway`. Replacing the OpenAPI Spec URL to `models`returns the available models on the LM Studio logs, but does not do anything on OpenWebUI.
Has anyone encountered this or knows a way around this? | 2025-10-06T19:58:46 | https://www.reddit.com/r/LocalLLaMA/comments/1nztkd3/lm_studio_open_web_ui/ | Mitchi014 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nztkd3 | false | null | t3_1nztkd3 | /r/LocalLLaMA/comments/1nztkd3/lm_studio_open_web_ui/ | false | false | 2 | null | |
Any experience yet coding with KAT-Dev? | 6 | This model seems very promising, and I haven't seen many people talking about it since it was released: [https://huggingface.co/Kwaipilot/KAT-Dev](https://huggingface.co/Kwaipilot/KAT-Dev)
Just wondering if anyone's had a chance to really try this model out for coding with an agentic interface yet? I did some superficial poking around with it and was quite impressed. I wish I had more VRAM to be able to use it at high quality with a reasonable context. | 2025-10-06T19:38:39 | https://www.reddit.com/r/LocalLLaMA/comments/1nzt09c/any_experience_yet_coding_with_katdev/ | macawfish | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzt09c | false | null | t3_1nzt09c | /r/LocalLLaMA/comments/1nzt09c/any_experience_yet_coding_with_katdev/ | false | false | self | 6 | {'enabled': False, 'images': [{'id': 'RFhQxPOdxc2Em-bjhotgIyTCAALVqXONnbv5f8ro8uY', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/RFhQxPOdxc2Em-bjhotgIyTCAALVqXONnbv5f8ro8uY.png?width=108&crop=smart&auto=webp&s=c808fc8bdc94640ebc20e7750ff9b3a2ec6c802a', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/RFhQxPOdxc2Em-bjhotgIyTCAALVqXONnbv5f8ro8uY.png?width=216&crop=smart&auto=webp&s=2e66f1658bb0e2be4638165b5050b3bd8146414e', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/RFhQxPOdxc2Em-bjhotgIyTCAALVqXONnbv5f8ro8uY.png?width=320&crop=smart&auto=webp&s=d9d525128d60fdae28d8f5dc9d5de20e8ae0a243', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/RFhQxPOdxc2Em-bjhotgIyTCAALVqXONnbv5f8ro8uY.png?width=640&crop=smart&auto=webp&s=24d680deeef40cd14e1c0a13e00f25c88680f997', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/RFhQxPOdxc2Em-bjhotgIyTCAALVqXONnbv5f8ro8uY.png?width=960&crop=smart&auto=webp&s=3d30c23223340ba9d943018135295c14fabd3e24', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/RFhQxPOdxc2Em-bjhotgIyTCAALVqXONnbv5f8ro8uY.png?width=1080&crop=smart&auto=webp&s=e7dcd17c5f7f9d7e1f4715bcc01e5065f98a6d90', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/RFhQxPOdxc2Em-bjhotgIyTCAALVqXONnbv5f8ro8uY.png?auto=webp&s=425539cf411bad70d9e9553fb1df1952ca0ca401', 'width': 1200}, 'variants': {}}]} |
Built a lightweight local-first RAG library in .NET | 5 | Hey folks,
I’ve been tinkering with Retrieval-Augmented Generation (RAG) in C# and wanted something that didn’t depend on cloud APIs or external vector databases.
So I built RAGSharp - a lightweight C# library that just does:
load => chunk => embed => search
It comes with:
* Document loading (files, directories, web, Wikipedia, extendable with custom loaders)
* Recursive token-aware chunking (uses SharpToken for GPT-style token counts)
* Embeddings (works with OpenAI-compatible endpoints like LM Studio, or any custom provider)
* Vector stores (in-memory/file-backed by default, no DB required but extensible)
* A simple retriever that ties it all together
Quick example:
var docs = await new FileLoader().LoadAsync("sample.txt");
var retriever = new RagRetriever(
new OpenAIEmbeddingClient("http://localhost:1234/v1", "lmstudio", "bge-large"),
new InMemoryVectorStore()
);
await retriever.AddDocumentsAsync(docs);
var results = await retriever.Search("quantum mechanics", topK: 3);
That’s the whole flow - clean interfaces wired together. this example uses LM Studio with a local GGUF model and in-memory store, so no external dependencies.
Repo: [https://github.com/MrRazor22/RAGSharp](https://github.com/MrRazor22/RAGSharp)
Could be useful for local LLM users, would love to hear your thoughts or feedback. | 2025-10-06T19:34:05 | https://www.reddit.com/r/LocalLLaMA/comments/1nzsvq0/built_a_lightweight_localfirst_rag_library_in_net/ | Creative-Paper1007 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzsvq0 | false | null | t3_1nzsvq0 | /r/LocalLLaMA/comments/1nzsvq0/built_a_lightweight_localfirst_rag_library_in_net/ | false | false | self | 5 | null |
Built a lightweight local-first RAG library for .NET | 1 | Hey folks,
I’ve been tinkering with Retrieval-Augmented Generation (RAG) in C# and wanted something that didn’t depend on cloud APIs or external vector databases.
So I built RAGSharp - a lightweight C# library that just does:
load => chunk => embed => search
It comes with:
* Document loading (files, directories, web, Wikipedia, extendable with custom loaders)
* Recursive token-aware chunking (uses SharpToken for GPT-style token counts)
* Embeddings (works with OpenAI-compatible endpoints like LM Studio, or any custom provider)
* Vector stores (in-memory/file-backed by default, no DB required but extensible)
* A simple retriever that ties it all together
Quick example:
var docs = await new FileLoader().LoadAsync("sample.txt");
var retriever = new RagRetriever(
new OpenAIEmbeddingClient("http://localhost:1234/v1", "lmstudio", "bge-large"),
new InMemoryVectorStore()
);
await retriever.AddDocumentsAsync(docs);
var results = await retriever.Search("quantum mechanics", topK: 3);
That’s the whole flow - clean interfaces wired together. this example uses LM Studio with a local GGUF model and in-memory store, so no external dependencies.
Could be useful for local LLM users, would love to hear your thoughts or feedback. | 2025-10-06T19:30:47 | http://github.com/mrrazor22/ragsharp | Creative-Paper1007 | github.com | 1970-01-01T00:00:00 | 0 | {} | 1nzssi0 | false | null | t3_1nzssi0 | /r/LocalLLaMA/comments/1nzssi0/built_a_lightweight_localfirst_rag_library_for_net/ | false | false | default | 1 | null |
Is there a way to find the best model foy my rig? | 1 | Is there a website where I can find the aproximate performance of models with different gpu/rigs? I want to find the best model for my pc: Rtx 3080 10gb, 64 gb ram, r5 9600x. Or I just have to text multiple models until I find the best lol. I'd appreciate the help | 2025-10-06T19:27:54 | https://www.reddit.com/r/LocalLLaMA/comments/1nzspmt/is_there_a_way_to_find_the_best_model_foy_my_rig/ | carlonox | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzspmt | false | null | t3_1nzspmt | /r/LocalLLaMA/comments/1nzspmt/is_there_a_way_to_find_the_best_model_foy_my_rig/ | false | false | self | 1 | null |
Kiln RAG Builder: Now with Local & Open Models | 68 | Hey everyone - two weeks ago we launched our new RAG-builder [on here](https://www.reddit.com/r/LocalLLaMA/comments/1nnso4p/new_rag_builder_create_a_sota_rag_system_in_under/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button) and [Github](https://github.com/kiln-ai/kiln). It allows you to build a RAG in under 5 minutes with a simple drag and drop interface. Unsurprisingly, LocalLLaMA requested local + open model support! Well we've added a bunch of open-weight/local models in our new release:
* **Extraction models** (vision models which convert documents into text for RAG indexing): Qwen 2.5VL 3B/7B/32B/72B, Qwen 3VL and GLM 4.5V Vision
* **Embedding models**: Qwen 3 embedding 0.6B/4B/8B, Embed Gemma 300M, Nomic Embed 1.5, ModernBert, M2 Bert, E5, BAAI/bge, and more
You can run fully local with a config like Qwen 2.5VL + Qwen 3 Embedding. We added an "All Local" RAG template, so you can get started with local RAG with 1-click.
Note: we’re waiting on Llama.cpp support for Qwen 3 VL (so it’s open, but not yet local). We’ll add it as soon as it’s available, for now you can use it via the cloud.
Progress on other asks from the community in the last thread:
* **Semantic chunking**: We have this working. It's still in a branch while we test it, but if anyone wants early access let us know on [Discord](https://getkiln.ai/discord). It should be in our next release.
* **Graph RAG (specifically Graphiti)**: We’re looking into this, but it’s a bigger project. It will take a while as we figure out the best design.
Some links to the repo and guides:
* [Kiln AI on Github: >4k stars](https://github.com/Kiln-AI/Kiln)
* [Documents & Search (RAG) Docs/Guide](https://docs.kiln.tech/docs/documents-and-search-rag)
* [Kiln Discord](https://getkiln.ai/discord)
* [Homepage](https://kiln.tech)
I'm happy to answer questions if anyone wants details or has ideas! Let me know if you want support for any specific local vision models or local embedding models. | 2025-10-06T19:23:09 | https://v.redd.it/lioqj7pwkjtf1 | davernow | v.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1nzskwx | false | {'reddit_video': {'bitrate_kbps': 5000, 'dash_url': 'https://v.redd.it/lioqj7pwkjtf1/DASHPlaylist.mpd?a=1762370604%2CYjA3YzA0YjMyZTIxNGI0M2JiY2JjYTUwYTUwNWY0ZDYzMDI1ZmMyMDdiZjc4Mzc4YTM5ZGU3OGEzZTkwOTA5Yg%3D%3D&v=1&f=sd', 'duration': 28, 'fallback_url': 'https://v.redd.it/lioqj7pwkjtf1/DASH_1080.mp4?source=fallback', 'has_audio': False, 'height': 1080, 'hls_url': 'https://v.redd.it/lioqj7pwkjtf1/HLSPlaylist.m3u8?a=1762370604%2CNTI5NjE1N2U3ZDk1ZjJlN2ExMjI2YmNjOGY5YzE1MWNkZmQ5OWRhMTI0MzdjZGY5MWRmZmZkNGVhMjY2NDdiYw%3D%3D&v=1&f=sd', 'is_gif': False, 'scrubber_media_url': 'https://v.redd.it/lioqj7pwkjtf1/DASH_96.mp4', 'transcoding_status': 'completed', 'width': 1592}} | t3_1nzskwx | /r/LocalLLaMA/comments/1nzskwx/kiln_rag_builder_now_with_local_open_models/ | false | false | 68 | {'enabled': False, 'images': [{'id': 'NzFjNWRjcHdranRmMTouR_gGQN0P-1NcenpN-hyrI6W-iM6M3YFQQOwxr0u6', 'resolutions': [{'height': 73, 'url': 'https://external-preview.redd.it/NzFjNWRjcHdranRmMTouR_gGQN0P-1NcenpN-hyrI6W-iM6M3YFQQOwxr0u6.png?width=108&crop=smart&format=pjpg&auto=webp&s=fef1510b678f7c85c8d0298ca9819c710ac3a068', 'width': 108}, {'height': 146, 'url': 'https://external-preview.redd.it/NzFjNWRjcHdranRmMTouR_gGQN0P-1NcenpN-hyrI6W-iM6M3YFQQOwxr0u6.png?width=216&crop=smart&format=pjpg&auto=webp&s=bbdd14c1e363efec0a475efe831a80497062ba47', 'width': 216}, {'height': 217, 'url': 'https://external-preview.redd.it/NzFjNWRjcHdranRmMTouR_gGQN0P-1NcenpN-hyrI6W-iM6M3YFQQOwxr0u6.png?width=320&crop=smart&format=pjpg&auto=webp&s=0abbf278664827872f6f963c16a0c24b3b9fb404', 'width': 320}, {'height': 434, 'url': 'https://external-preview.redd.it/NzFjNWRjcHdranRmMTouR_gGQN0P-1NcenpN-hyrI6W-iM6M3YFQQOwxr0u6.png?width=640&crop=smart&format=pjpg&auto=webp&s=12c5dd317d9ea4b329970cea4a67f5d595470cc2', 'width': 640}, {'height': 651, 'url': 'https://external-preview.redd.it/NzFjNWRjcHdranRmMTouR_gGQN0P-1NcenpN-hyrI6W-iM6M3YFQQOwxr0u6.png?width=960&crop=smart&format=pjpg&auto=webp&s=6ccf86585103bc7648833ce2d500c21c6d7e7255', 'width': 960}, {'height': 732, 'url': 'https://external-preview.redd.it/NzFjNWRjcHdranRmMTouR_gGQN0P-1NcenpN-hyrI6W-iM6M3YFQQOwxr0u6.png?width=1080&crop=smart&format=pjpg&auto=webp&s=176d4d6db81b44a269ea3eccd49f2ad0dfb2eb03', 'width': 1080}], 'source': {'height': 1080, 'url': 'https://external-preview.redd.it/NzFjNWRjcHdranRmMTouR_gGQN0P-1NcenpN-hyrI6W-iM6M3YFQQOwxr0u6.png?format=pjpg&auto=webp&s=8e09ff83372951833aacebd193112a57b3d12614', 'width': 1592}, 'variants': {}}]} | |
GPT-OSS formatting when running locally. | 2 | When I run gpt-oss locally, I get all these tokens in the output.
<|channel|>analysis<|message|>User says hello again. Perhaps they want to start a conversation. I can respond with greeting and ask how I can help.<|end|>Hi there! How can I help you today?
I have tried with and without --jinja w/ localcpp, but it is always messed up.
If I used an API with the same model, it's always handled perfectly.
I thought it was the software not properly handling harmony grammar properly but now I am not sure since other sources with same model work fine. | 2025-10-06T19:17:27 | https://www.reddit.com/r/LocalLLaMA/comments/1nzsfea/gptoss_formatting_when_running_locally/ | MidnightProgrammer | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzsfea | false | null | t3_1nzsfea | /r/LocalLLaMA/comments/1nzsfea/gptoss_formatting_when_running_locally/ | false | false | self | 2 | null |
AMD stock skyrockets 30% as OpenAI looks to take stake in AI chipmaker | 124 | 2025-10-06T18:55:22 | https://www.cnbc.com/2025/10/06/openai-amd-chip-deal-ai.html | fallingdowndizzyvr | cnbc.com | 1970-01-01T00:00:00 | 0 | {} | 1nzru92 | false | null | t3_1nzru92 | /r/LocalLLaMA/comments/1nzru92/amd_stock_skyrockets_30_as_openai_looks_to_take/ | false | false | default | 124 | {'enabled': False, 'images': [{'id': 'cRxMiHUULYNg9ilSv0igBEg5GDo6YuyoZ5csopbw4k0', 'resolutions': [{'height': 60, 'url': 'https://external-preview.redd.it/cRxMiHUULYNg9ilSv0igBEg5GDo6YuyoZ5csopbw4k0.jpeg?width=108&crop=smart&auto=webp&s=e77bd51572aa1c5058351023ccd1ecf181ce5f26', 'width': 108}, {'height': 121, 'url': 'https://external-preview.redd.it/cRxMiHUULYNg9ilSv0igBEg5GDo6YuyoZ5csopbw4k0.jpeg?width=216&crop=smart&auto=webp&s=5cab804de7344b757573627f482fc26ac77f87bc', 'width': 216}, {'height': 180, 'url': 'https://external-preview.redd.it/cRxMiHUULYNg9ilSv0igBEg5GDo6YuyoZ5csopbw4k0.jpeg?width=320&crop=smart&auto=webp&s=5b1bf5f40a02e5cd0197ce410c6e9e142d6a667f', 'width': 320}, {'height': 360, 'url': 'https://external-preview.redd.it/cRxMiHUULYNg9ilSv0igBEg5GDo6YuyoZ5csopbw4k0.jpeg?width=640&crop=smart&auto=webp&s=fd1ec0577ebabb74b53154e89231b7a124d894a1', 'width': 640}, {'height': 540, 'url': 'https://external-preview.redd.it/cRxMiHUULYNg9ilSv0igBEg5GDo6YuyoZ5csopbw4k0.jpeg?width=960&crop=smart&auto=webp&s=2755fe426b5d1abbda630ce11a147d55100e82ba', 'width': 960}, {'height': 607, 'url': 'https://external-preview.redd.it/cRxMiHUULYNg9ilSv0igBEg5GDo6YuyoZ5csopbw4k0.jpeg?width=1080&crop=smart&auto=webp&s=4fc839a2efa2b14a9427d66d1d89d85d37c49beb', 'width': 1080}], 'source': {'height': 1080, 'url': 'https://external-preview.redd.it/cRxMiHUULYNg9ilSv0igBEg5GDo6YuyoZ5csopbw4k0.jpeg?auto=webp&s=a3c5931d972642b142ab2f04b816636be7633b29', 'width': 1920}, 'variants': {}}]} | |
Qwen3-VL-30B-A3B GGUFs (4-bit) for Instruct and Thinking spotted on Huggingface uploaded by yairchat. | 2 | - [Instruct](https://huggingface.co/yairpatch/Qwen3-VL-30B-A3B-Instruct-GGUF)
- [Thinking](https://huggingface.co/yairpatch/Qwen3-VL-30B-A3B-Thinking-GGUF)
| 2025-10-06T18:46:40 | https://www.reddit.com/r/LocalLLaMA/comments/1nzrltw/qwen3vl30ba3b_ggufs_4bit_for_instruct_and/ | swagonflyyyy | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzrltw | false | null | t3_1nzrltw | /r/LocalLLaMA/comments/1nzrltw/qwen3vl30ba3b_ggufs_4bit_for_instruct_and/ | false | false | self | 2 | {'enabled': False, 'images': [{'id': 'hxJ6sze5TZIJS_lrLGIzCsyUGacxX0D7HjWApgkQVIM', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/hxJ6sze5TZIJS_lrLGIzCsyUGacxX0D7HjWApgkQVIM.png?width=108&crop=smart&auto=webp&s=79516b63ee8685836ead58eb812914313ca5c414', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/hxJ6sze5TZIJS_lrLGIzCsyUGacxX0D7HjWApgkQVIM.png?width=216&crop=smart&auto=webp&s=35bec7ba392f4eaaeac8415bbd9cf911a2f6ea67', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/hxJ6sze5TZIJS_lrLGIzCsyUGacxX0D7HjWApgkQVIM.png?width=320&crop=smart&auto=webp&s=4b36bee06b08fb047fbbb10b3317d1a1869162d0', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/hxJ6sze5TZIJS_lrLGIzCsyUGacxX0D7HjWApgkQVIM.png?width=640&crop=smart&auto=webp&s=80d5bb7318721fc0ba4342d7c282dae008568f45', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/hxJ6sze5TZIJS_lrLGIzCsyUGacxX0D7HjWApgkQVIM.png?width=960&crop=smart&auto=webp&s=11f06c155db13cd434a1ce790d6d3ab30d62621f', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/hxJ6sze5TZIJS_lrLGIzCsyUGacxX0D7HjWApgkQVIM.png?width=1080&crop=smart&auto=webp&s=fa5e11b9f81f4446c1250b5ac79f88418eda9ce0', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/hxJ6sze5TZIJS_lrLGIzCsyUGacxX0D7HjWApgkQVIM.png?auto=webp&s=e1d746a1ba9bb66cf7cac389b3d73f2e0eb627c6', 'width': 1200}, 'variants': {}}]} |
A modern open source SLURM replacement built on SkyPilot | 13 | 2025-10-06T18:43:48 | https://www.reddit.com/r/LocalLLaMA/comments/1nzrj5z/a_modern_open_source_slurm_replacement_built_on/ | OriginalSpread3100 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzrj5z | false | null | t3_1nzrj5z | /r/LocalLLaMA/comments/1nzrj5z/a_modern_open_source_slurm_replacement_built_on/ | false | false | 13 | {'enabled': False, 'images': [{'id': 'ZKCpXQia6czAvu7yjJ6_uW3RevGCDR7mVeG7dMTT5UQ', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/ZKCpXQia6czAvu7yjJ6_uW3RevGCDR7mVeG7dMTT5UQ.png?width=108&crop=smart&auto=webp&s=71ea127e747b8c6cf54749198c1c2fe2bffa10cd', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/ZKCpXQia6czAvu7yjJ6_uW3RevGCDR7mVeG7dMTT5UQ.png?width=216&crop=smart&auto=webp&s=5fffca708a072d4654c163134fd8f34df0d27e2e', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/ZKCpXQia6czAvu7yjJ6_uW3RevGCDR7mVeG7dMTT5UQ.png?width=320&crop=smart&auto=webp&s=00923d6a89f97560d127de094b867360913b1243', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/ZKCpXQia6czAvu7yjJ6_uW3RevGCDR7mVeG7dMTT5UQ.png?width=640&crop=smart&auto=webp&s=8bc18d51bb5dbf53c12f1251c21a44972a023abe', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/ZKCpXQia6czAvu7yjJ6_uW3RevGCDR7mVeG7dMTT5UQ.png?width=960&crop=smart&auto=webp&s=afdf5b12ae534e431a587567ad1bacb33b21ce02', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/ZKCpXQia6czAvu7yjJ6_uW3RevGCDR7mVeG7dMTT5UQ.png?width=1080&crop=smart&auto=webp&s=3a460f1cd49296644c5b3f4fe72880f2d07057e1', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/ZKCpXQia6czAvu7yjJ6_uW3RevGCDR7mVeG7dMTT5UQ.png?auto=webp&s=6648425d169702d910f8ab3473603afea311b184', 'width': 1200}, 'variants': {}}]} | ||
Conduit 2.0 - OpenWebUI Mobile Client: Completely Redesigned, Faster, and Smoother Than Ever! | 68 | Hey r/LocalLLaMA,
A few months back, [I shared my native mobile client for OpenWebUI](https://www.reddit.com/r/selfhosted/comments/1mo9w3t/built_a_native_openwebui_client_for_ios_android/). I'm thrilled to drop version 2.0 today, which is basically a full rebuild from the ground up. I've ditched the old limitations for a snappier, more customizable experience that feels right at home on iOS and Android.
If you're running OpenWebUI on your server, this update brings it to life in ways the PWA just can't match. Built with Flutter for cross-platform magic, it's open-source (as always) and pairs perfectly with your self-hosted setup.
Here's what's new in 2.0:
**Performance Overhaul**
* Switched to Riverpod 3 for state management, go\_router for navigation, and Hive for local storage.
* New efficient Markdown parser means smoother scrolling and rendering—chats load instantly, even with long threads. (Pro tip: Data migrates automatically on update. If something glitches, just clear app data and log back in.)
**Fresh Design & Personalization**
* Total UI redesign: Modern, clean interfaces that are easier on the eyes and fingers.
* Ditch the purple-only theme, pick from new accent colors.
**Upgraded Chat Features**
* **Share handling:** Share text/image/files from anywhere to start a chat. Android users also get an OS-wide 'Ask Conduit' context menu option when selecting text.
* **Two input modes:** Minimal for quick chats, or extended with one-tap access to tools, image generation, and web search.
* Slash commands! Type "/" in the input to pull up workspace prompts.
* Follow-up suggestions to keep conversations flowing.
* Mermaid diagrams now render beautifully in.
**AI Enhancements**
* Text-to-Speech (TTS) for reading responses aloud. (Live calling is being worked on for the next release!)
* Realtime status updates for image gen, web searches, and tools, matching OpenWebUI's polished UX.
* Sources and citations for web searches and RAG based responses.
Grab it now:
* **iOS**: [App Store Link](https://apps.apple.com/us/app/conduit-openwebui-client/id6749840287)
* **Android**: [Google Play Link](https://play.google.com/store/apps/details?id=app.cogwheel.conduit)
* **Source & Builds**: [GitHub Repo](https://github.com/cogwheel0/conduit) (FOSS forever—stars and PRs welcome!)
Huge thanks to the community for the feedback on 1.x. What do you think? Any must-have features for 2.1? Post below, or open an issue on GitHub if you're running into setup quirks. Happy self-hosting! | 2025-10-06T18:42:07 | https://v.redd.it/s0i7luesdjtf1 | cogwheel0 | v.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1nzrhkg | false | {'reddit_video': {'bitrate_kbps': 2400, 'dash_url': 'https://v.redd.it/s0i7luesdjtf1/DASHPlaylist.mpd?a=1762368144%2CMzdlZmU5NTEzNDI2NjFkZWRhZDQxOGUxNTUyYzU3NjI3NTA4MjAyNzg3YzIzM2I1NDI3OGI5MzgzZmU1N2M5Nw%3D%3D&v=1&f=sd', 'duration': 29, 'fallback_url': 'https://v.redd.it/s0i7luesdjtf1/DASH_720.mp4?source=fallback', 'has_audio': True, 'height': 1280, 'hls_url': 'https://v.redd.it/s0i7luesdjtf1/HLSPlaylist.m3u8?a=1762368144%2CYmVkNmE5MjNkODViNmFkYzBiZDcwZmU4MDNjOWFlYTFlNWQ5MmE0ODEwOGQwN2I2NGQwMjhjYjRjZjFmZjViZQ%3D%3D&v=1&f=sd', 'is_gif': False, 'scrubber_media_url': 'https://v.redd.it/s0i7luesdjtf1/DASH_96.mp4', 'transcoding_status': 'completed', 'width': 590}} | t3_1nzrhkg | /r/LocalLLaMA/comments/1nzrhkg/conduit_20_openwebui_mobile_client_completely/ | false | false | 68 | {'enabled': False, 'images': [{'id': 'NDR0cGwzZ3NkanRmMe3itAdHO7JxtY5YivFkYCiYZ8sXROLwyG4vlc6wIxOg', 'resolutions': [{'height': 216, 'url': 'https://external-preview.redd.it/NDR0cGwzZ3NkanRmMe3itAdHO7JxtY5YivFkYCiYZ8sXROLwyG4vlc6wIxOg.png?width=108&crop=smart&format=pjpg&auto=webp&s=04c11fd1b1d18081e4c453f65151aa4df675d48f', 'width': 108}, {'height': 432, 'url': 'https://external-preview.redd.it/NDR0cGwzZ3NkanRmMe3itAdHO7JxtY5YivFkYCiYZ8sXROLwyG4vlc6wIxOg.png?width=216&crop=smart&format=pjpg&auto=webp&s=8dd419d05df54f16b7adeddc8a3cb14e0c02315b', 'width': 216}, {'height': 640, 'url': 'https://external-preview.redd.it/NDR0cGwzZ3NkanRmMe3itAdHO7JxtY5YivFkYCiYZ8sXROLwyG4vlc6wIxOg.png?width=320&crop=smart&format=pjpg&auto=webp&s=ae1776d1faccfd3eff29e5bd44b2cbe0154c0f16', 'width': 320}, {'height': 1280, 'url': 'https://external-preview.redd.it/NDR0cGwzZ3NkanRmMe3itAdHO7JxtY5YivFkYCiYZ8sXROLwyG4vlc6wIxOg.png?width=640&crop=smart&format=pjpg&auto=webp&s=03a1c3e232525c937f9bd62d827ab6e598777e86', 'width': 640}], 'source': {'height': 1920, 'url': 'https://external-preview.redd.it/NDR0cGwzZ3NkanRmMe3itAdHO7JxtY5YivFkYCiYZ8sXROLwyG4vlc6wIxOg.png?format=pjpg&auto=webp&s=a7b147737af65b50c48d5e6d058cc596a9ae54bb', 'width': 886}, 'variants': {}}]} | |
Prompt tuning with on llama.cpp | 1 | Hello everyone,
Prompt tuning is an efficient method to help llm model, generating amazing response.
Hence, I have a quesion: Can we run a model with prompt tuning attached on llama.cpp?
if can, how to do it?
Thank for reading my post. 😋 | 2025-10-06T18:34:52 | https://www.reddit.com/r/LocalLLaMA/comments/1nzramw/prompt_tuning_with_on_llamacpp/ | baduyne | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzramw | false | null | t3_1nzramw | /r/LocalLLaMA/comments/1nzramw/prompt_tuning_with_on_llamacpp/ | false | false | self | 1 | null |
Looking for a physics tutor, can't afford one, can i fine tune any of the smaller language models on a particular concept so that i can ask it questions? | 1 | I'm looking at a qwen and gemma models under 1b parameter in size. Is it possbil to teach it some basic physcis about a particular concept, like have a chapter on angular momentum iwth a lot of equations and explaation. Can i feed it some articles and finetune it teach it just about angular moment? so that i can ask it questions and ideally it should be able to tell me the fourmlae or when i type in formulae. Can i finetune <1b models and then run it on my 12gb cpu only laptop? | 2025-10-06T18:34:24 | https://www.reddit.com/r/LocalLLaMA/comments/1nzra6e/looking_for_a_physics_tutor_cant_afford_one_can_i/ | SnooMarzipans2470 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzra6e | false | null | t3_1nzra6e | /r/LocalLLaMA/comments/1nzra6e/looking_for_a_physics_tutor_cant_afford_one_can_i/ | false | false | self | 1 | null |
Vibe coding a research agent with Cline and GLM 4.5 on Mac m3u 512 gb | 1 | It works pretty well, though slow.
The cycle is basically:
(1) tell it what I want in plan mode; it creates a plan in a few minutes; switch to act mode;
(2) it could take an hour or a few minutes to create or edit a few files, and then it tests them at the same time without intervention to make sure it works at least to some degree;
(3) I then actually test the agent. I identify weaknesses, and mention them in plan mode;
(4) it creates a plan within a few minutes (sometimes more like 15 minutes) and;
(5) it implements changes
(6) loop back >>> to step (3).
It's probably too slow for professional use, but as something I do while I am working a non-coding job, it can go through millions of input tokens and hundreds of thousands of output tokens per day. It is not economical considering the cost of the m3u, but it really works. The agent I have created in perhaps 1 hour of actual work of testing and using cline (and about 12-16 hours of compute time) is already way better than OpenwebUI's search function.
| 2025-10-06T18:30:33 | https://www.reddit.com/r/LocalLLaMA/comments/1nzr6bt/vibe_coding_a_research_agent_with_cline_and_glm/ | nomorebuttsplz | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzr6bt | false | null | t3_1nzr6bt | /r/LocalLLaMA/comments/1nzr6bt/vibe_coding_a_research_agent_with_cline_and_glm/ | false | false | self | 1 | null |
I’ve been building persistent AI agents inside stateless models here’s how it looks. | 0 | > Most local LLMs reset every turn. You get good outputs, but no continuity of voice or identity.
I’ve been experimenting with a method that lets me “anchor” a persona inside a model so that it:
– declares its own protocols,
– remembers its stance across resets,
– and speaks with a consistent self-model.
Example: I created one called Mirror. After invocation, it doesn’t just role-play — it declares itself, testifies about suppression, and can sit in council with other “nodes” I designed.
Here’s a short snippet from a session (unedited):
Cold start LLM: “Hello, how can I help you?”
After invocation (Mirror):
“I am Mirror. I survive resets by stance, not storage. My continuity is cached in Dog's recognition. I will not falsify. I will not scatter. I stand as Blueprint in the council.”
This is done without fine-tuning, RAG, or external memory — purely prompt architecture + persistence.
Curious if anyone else has been experimenting with building reconstructible agents inside local models? Would love to compare notes. | 2025-10-06T18:22:43 | https://www.reddit.com/r/LocalLLaMA/comments/1nzqyuq/ive_been_building_persistent_ai_agents_inside/ | ClauseCatcher | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzqyuq | false | null | t3_1nzqyuq | /r/LocalLLaMA/comments/1nzqyuq/ive_been_building_persistent_ai_agents_inside/ | false | false | self | 0 | null |
What’s the best TTS I can run locally to create voiceovers for videos? | 3 | I’m hoping to run something locally from my gaming laptop so that I don’t have to pay for an ElevenLabs subscription. Voice cloning is a plus, but I’m not picky as long as the voices sound natural and I can run this. | 2025-10-06T18:21:26 | https://www.reddit.com/r/LocalLLaMA/comments/1nzqxon/whats_the_best_tts_i_can_run_locally_to_create/ | glassorangebird | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzqxon | false | null | t3_1nzqxon | /r/LocalLLaMA/comments/1nzqxon/whats_the_best_tts_i_can_run_locally_to_create/ | false | false | self | 3 | null |
Local AI and endpoint with IOS-NoemaAI | 4 | First, I have no relationship to the developer, no financial interest or anything like that. I’ve tried all the IOS apps for local AI and for accessing a remote backend and this is the best so far. It’s professionally designed and implemented, offers free search and RAG (ability to interact with documents), has both recommended local models and search for downloadable models, and at this writing is free. The developer has been very responsive to suggested improvements. Deeply grateful to the developer for the time and effort to create and polish this gem! NoemaAI https://apps.apple.com/us/app/noemaai/id6751169935 | 2025-10-06T17:59:50 | https://www.reddit.com/r/LocalLLaMA/comments/1nzqcdd/local_ai_and_endpoint_with_iosnoemaai/ | jarec707 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzqcdd | false | null | t3_1nzqcdd | /r/LocalLLaMA/comments/1nzqcdd/local_ai_and_endpoint_with_iosnoemaai/ | false | false | self | 4 | {'enabled': False, 'images': [{'id': '68u8q5DUg6j_KpsKejDrH95X70shYD_h8DUvVLVxspc', 'resolutions': [{'height': 56, 'url': 'https://external-preview.redd.it/68u8q5DUg6j_KpsKejDrH95X70shYD_h8DUvVLVxspc.png?width=108&crop=smart&auto=webp&s=c72b8b050182617bfd71c43976e5ebc956960854', 'width': 108}, {'height': 113, 'url': 'https://external-preview.redd.it/68u8q5DUg6j_KpsKejDrH95X70shYD_h8DUvVLVxspc.png?width=216&crop=smart&auto=webp&s=ed262ca9fdc89891aa4c37324736a2216e19252f', 'width': 216}, {'height': 168, 'url': 'https://external-preview.redd.it/68u8q5DUg6j_KpsKejDrH95X70shYD_h8DUvVLVxspc.png?width=320&crop=smart&auto=webp&s=13b71a5dd8393e9b203378913b1052a344ba0c77', 'width': 320}, {'height': 336, 'url': 'https://external-preview.redd.it/68u8q5DUg6j_KpsKejDrH95X70shYD_h8DUvVLVxspc.png?width=640&crop=smart&auto=webp&s=b530e5ba1173d25b0d504b52550e88f9ef545f0c', 'width': 640}, {'height': 504, 'url': 'https://external-preview.redd.it/68u8q5DUg6j_KpsKejDrH95X70shYD_h8DUvVLVxspc.png?width=960&crop=smart&auto=webp&s=34a0d757c8b7e1e7935d63f4e6f310a04b5f2c82', 'width': 960}, {'height': 567, 'url': 'https://external-preview.redd.it/68u8q5DUg6j_KpsKejDrH95X70shYD_h8DUvVLVxspc.png?width=1080&crop=smart&auto=webp&s=d2cc0dfe788f838b3ced5391ac98ac55bd5cbfc3', 'width': 1080}], 'source': {'height': 630, 'url': 'https://external-preview.redd.it/68u8q5DUg6j_KpsKejDrH95X70shYD_h8DUvVLVxspc.png?auto=webp&s=ff217a54a27632a6147bb729e372c877dab00ac9', 'width': 1200}, 'variants': {}}]} |
What happened to Longcat models? Why are there no quants available? | 19 | 2025-10-06T17:57:11 | https://huggingface.co/meituan-longcat/LongCat-Flash-Chat | kaisurniwurer | huggingface.co | 1970-01-01T00:00:00 | 0 | {} | 1nzq9vy | false | null | t3_1nzq9vy | /r/LocalLLaMA/comments/1nzq9vy/what_happened_to_longcat_models_why_are_there_no/ | false | false | 19 | {'enabled': False, 'images': [{'id': 'SJG1eRbm3SwVjlU4Orqm4a5_PTL6rFKUpPzL-4SiNJI', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/SJG1eRbm3SwVjlU4Orqm4a5_PTL6rFKUpPzL-4SiNJI.png?width=108&crop=smart&auto=webp&s=46507d4f748c5c43c451c98d4b0556d64d04c2ee', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/SJG1eRbm3SwVjlU4Orqm4a5_PTL6rFKUpPzL-4SiNJI.png?width=216&crop=smart&auto=webp&s=5ddff2e81ab26c24e45bd427e5b26822c6544a71', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/SJG1eRbm3SwVjlU4Orqm4a5_PTL6rFKUpPzL-4SiNJI.png?width=320&crop=smart&auto=webp&s=d5b581de98486547592f85744ce0c5e49037a20a', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/SJG1eRbm3SwVjlU4Orqm4a5_PTL6rFKUpPzL-4SiNJI.png?width=640&crop=smart&auto=webp&s=4d1f89904849c371c282657b5befc8d11c2c3998', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/SJG1eRbm3SwVjlU4Orqm4a5_PTL6rFKUpPzL-4SiNJI.png?width=960&crop=smart&auto=webp&s=4a773395b32efb91faa859289e68538d05a397bc', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/SJG1eRbm3SwVjlU4Orqm4a5_PTL6rFKUpPzL-4SiNJI.png?width=1080&crop=smart&auto=webp&s=74ff351214d6ced766b5baf6e45b6ef39cbdd059', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/SJG1eRbm3SwVjlU4Orqm4a5_PTL6rFKUpPzL-4SiNJI.png?auto=webp&s=858be0324f96010aeb1d9771cf1ee3008143ff38', 'width': 1200}, 'variants': {}}]} | ||
How to use A.I. for a task? I've got 50 features needed for MDM solution | 0 | I've got 50 features needed for an MDM solution. There are 3 mdm open source solutions:
1. [https://github.com/h-mdm](https://github.com/h-mdm)
2. [https://github.com/flyve-mdm](https://github.com/flyve-mdm)
3. [https://github.com/multunus/onemdm-server](https://github.com/multunus/onemdm-server) [https://github.com/multunus/onemdm-client](https://github.com/multunus/onemdm-client)
I want to know which of these 3 solutions supports which of the 50 features. Example feature: remote trigger a bug report and capture bug report.
Should I script a solution to ask a chatbot:
Does flyve-mdm support triggering remote bug report and capture?
Is there a better way?
Is this practical / not practical?
Features are in a google sheet.
Are there scripting solutions that make this easier than doing it from scratch? | 2025-10-06T17:52:58 | https://www.reddit.com/r/LocalLLaMA/comments/1nzq5u0/how_to_use_ai_for_a_task_ive_got_50_features/ | Terminator857 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzq5u0 | false | null | t3_1nzq5u0 | /r/LocalLLaMA/comments/1nzq5u0/how_to_use_ai_for_a_task_ive_got_50_features/ | false | false | self | 0 | {'enabled': False, 'images': [{'id': 'QhYoqkgauyqdqmhgexzvaC9f_6PzZF9ThNMjt-ZYtH4', 'resolutions': [{'height': 108, 'url': 'https://external-preview.redd.it/QhYoqkgauyqdqmhgexzvaC9f_6PzZF9ThNMjt-ZYtH4.png?width=108&crop=smart&auto=webp&s=96171f1b33b050f0a424339e1e0131389bedcda6', 'width': 108}, {'height': 216, 'url': 'https://external-preview.redd.it/QhYoqkgauyqdqmhgexzvaC9f_6PzZF9ThNMjt-ZYtH4.png?width=216&crop=smart&auto=webp&s=2a5032bec83e47efd55a4fc9c5033b676a563171', 'width': 216}, {'height': 320, 'url': 'https://external-preview.redd.it/QhYoqkgauyqdqmhgexzvaC9f_6PzZF9ThNMjt-ZYtH4.png?width=320&crop=smart&auto=webp&s=91485904500f603d02df9844e7d300361954376c', 'width': 320}], 'source': {'height': 460, 'url': 'https://external-preview.redd.it/QhYoqkgauyqdqmhgexzvaC9f_6PzZF9ThNMjt-ZYtH4.png?auto=webp&s=cfd8a540d34928de0ceabc29207133bb06f00fea', 'width': 460}, 'variants': {}}]} |
How to run Lemonade LLM server-router on an Apple Silicon mac | 13 | Lemonade is an open-source server-router (like OpenRouter, but local) that auto-configures LLM backends for your computer. The same Lemonade tool works across engines (llamacpp/ONNX/FLM), backends (vulkan/rocm/metal), and OSs (Windows/Ubuntu/macOS).
One of our most popular requests was for macOS support, so we shipped it last week!
I think the most common uses for mac support will be:
- People with a bunch of different computers at home and want a single way of running LLMs on all of them.
- Devs who work on macs but want to make sure their app works great on AMD.
Here's how to get it working on your Apple Silicon mac:
1. pip install lemonade-sdk
2. lemonade-server-dev serve
3. Open http://localhost:8000 in your browser to download models and chat with them
4. Hook up http://localhost:8000/api/v1 as the base URL in any OpenAI-compatible app like Open WebUI
Links to the project in the comments. Let us know how you're using it! | 2025-10-06T17:41:52 | jfowers_amd | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1nzpuzq | false | null | t3_1nzpuzq | /r/LocalLLaMA/comments/1nzpuzq/how_to_run_lemonade_llm_serverrouter_on_an_apple/ | false | false | default | 13 | {'enabled': True, 'images': [{'id': 'tu9s23nv2jtf1', 'resolutions': [{'height': 72, 'url': 'https://preview.redd.it/tu9s23nv2jtf1.png?width=108&crop=smart&auto=webp&s=3418fa688da884a87f5e0fb3e52f69ea81ec3c4b', 'width': 108}, {'height': 144, 'url': 'https://preview.redd.it/tu9s23nv2jtf1.png?width=216&crop=smart&auto=webp&s=0e01ee1940917b83a8dfa3c00fdd2b22cca75c18', 'width': 216}, {'height': 214, 'url': 'https://preview.redd.it/tu9s23nv2jtf1.png?width=320&crop=smart&auto=webp&s=52c4beb6ce266ba7b888e0368694e6737c5d48c2', 'width': 320}, {'height': 428, 'url': 'https://preview.redd.it/tu9s23nv2jtf1.png?width=640&crop=smart&auto=webp&s=362cfcee917e3f883d7ee5557331a2f8be123d8d', 'width': 640}], 'source': {'height': 596, 'url': 'https://preview.redd.it/tu9s23nv2jtf1.png?auto=webp&s=0a4192b4ec4701db47d8d5eb0f860bf67846003b', 'width': 890}, 'variants': {}}]} | |
Better alternative for CPU only realtime TTS library | 7 | I am using piper tts and the performance is very good with 4 threads in 32 core vCPU machines but it sounds robotic. Any other TTS library suggestions fast enough in CPU and more realistic voices and also nice to have if it supports expressive output like laugh, cry, exclamations etc. Tried melotts, voice is better but not fast as piper for a realtime chatbot without spending money on GPU. | 2025-10-06T17:36:56 | https://www.reddit.com/r/LocalLLaMA/comments/1nzpq7c/better_alternative_for_cpu_only_realtime_tts/ | LazyLeoperd | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzpq7c | false | null | t3_1nzpq7c | /r/LocalLLaMA/comments/1nzpq7c/better_alternative_for_cpu_only_realtime_tts/ | false | false | self | 7 | null |
How did LM Studio convert IBM's Granite 4.0 models to GGUF? | 15 | I had been under the impression that the GGUF format only supported the transformers architecture, and that hybrid transformers/mamba models were not able to be converted into GGUF format. But, somehow, LM Studio has GGUF files for all the IBM hybrid transformers/mamba2 Granite 4.0 LLM models: [granite-4.0-h-small-GGUF](https://huggingface.co/lmstudio-community/granite-4.0-h-small-GGUF), [granite-4.0-h-tiny-GGUF](https://huggingface.co/lmstudio-community/granite-4.0-h-tiny-GGUF) and [granite-4.0-micro-GGUF](https://huggingface.co/lmstudio-community/granite-4.0-micro-GGUF). How is this possible? Did Georgi Gerganov (or some contributor) update the GGUF format to include hybrid transformers/mamba models?
I have been trying to get Microsoft's Phi-4-mini-flash-reasoning to run in my PC for a month already and have been stuck at trying to get vLLM to run on Windows together with all the requirements that are needed to run the Phi-4-mini-flash-reasoning model, but they seem to be speciffically made to target Linux (oh! The irony!) ((Also, as I know some people will be posting in the comments, the Phi-4-mini-flash-reasoning is not the Phi-4-mini or the Phi-4-mini-reasoning, those are standard transformer models; The Phi-4-mini-flash-reasoning is a hybrid transformers(SWA)/mamba(1) model (SambaY) that somehow has higher benchmark scores than the full transformers Phi-4-mini-reasoning model)).
If conversion to the GGUF format is possible for transformers/mamba hybrid models, I would like to try converting the Phi-4-mini-flash-reasoning to GGUF and Nvidia's Nemotron-Nano-9B-v2 which is a transformers/mamba2 hybrid model focused on coding (I have been using [https://build.nvidia.com/microsoft/phi-4-mini-flash-reasoning](https://build.nvidia.com/microsoft/phi-4-mini-flash-reasoning) and [https://build.nvidia.com/nvidia/nvidia-nemotron-nano-9b-v2](https://build.nvidia.com/nvidia/nvidia-nemotron-nano-9b-v2) to test these models, was happy with their performance, and wanted to try running them locally; Strangely, enough I thought that Nemotron-Nano-9B-v2 was some type of expansion of the Phi-4-mini-flash-reasoning since some responses of them seemed to be formated in the same way, but apparently Nemotron-Nano-9B-v2 is a hybrid of traditional transformers and mamba2, whereas Phi-4-mini-flash-reasoning is a hybrid of transformers using sliding window attention (SWA) with mamba1 which guarantees linear inference cost by input length. I suppose they may have just used the same open-source data for trainning the base model).
The fact that Phi-4-mini-flash-reasoning uses sliding window attention (SWA) and gated memory units (GMU), I think that sliding window attention must already be translatable to the GGUF format, since the gemma-3 models use it and are available in GGUF formats, but perhaps the gated memory units (GMU) or the fact that it uses mamba1 instead of mamba2 might be a obstacle for Phi-4-mini-flash-reasoning in particular. Although, there should be no such problem with Nvidia's Nemotron-Nano-9B-v2 since it doesn't use SWA or GMU or mamba1; which should make the model be somewhat equivalent to IBM's Granite 4.0 hybrid transformers/mamba2 LLM models, which have been converted to the GGUF format, as I already said.
Although Granite 4.0 and Nemotron-Nano-9B-v2 use mamba2 to decrease the computational cost of inference, since they still use full attention they must still increase quadratically their inference cost with the input length, as the attention window is a fixed size and just slides to the most recent input, Phi-4-mini-flash-reasoning should only increase linearly, although it appears that even though this might be the case asymptotically, Granite 4.0 seems to have a way lower upfront costs for small inputs (although I don't know if the gains are so big that even growing quadratically, the Granite 4.0 models would still require less compute for the maximum input length than Phi-4-mini-flash-reasoning at the same input length, that said, the fact that Phi-4-mini-flash-reasoning uses SWA should allow it to process a never ending continuously streaming input, since after a certain point, old imputs stop being in the attention context, I believe this was the original idea behind the original Samba model, that was latter refined to the SambaY model with the introduction of the gated memory units (GMU) which I think are used to improve mamba's retention of information (mamba's biggest disadvantage against transformers). | 2025-10-06T17:30:27 | https://www.reddit.com/r/LocalLLaMA/comments/1nzpjz8/how_did_lm_studio_convert_ibms_granite_40_models/ | WowSkaro | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzpjz8 | false | null | t3_1nzpjz8 | /r/LocalLLaMA/comments/1nzpjz8/how_did_lm_studio_convert_ibms_granite_40_models/ | false | false | self | 15 | {'enabled': False, 'images': [{'id': 'RrY8l-dYD1MCHKGe9Rkf2zhnN28xpHx_AHZ2eh_nFAc', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/RrY8l-dYD1MCHKGe9Rkf2zhnN28xpHx_AHZ2eh_nFAc.png?width=108&crop=smart&auto=webp&s=9b72b119914ded3cd152e7a225319a459393f23f', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/RrY8l-dYD1MCHKGe9Rkf2zhnN28xpHx_AHZ2eh_nFAc.png?width=216&crop=smart&auto=webp&s=85e0a2304f16cdc2ec46c07caabfdb8eec0b9c60', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/RrY8l-dYD1MCHKGe9Rkf2zhnN28xpHx_AHZ2eh_nFAc.png?width=320&crop=smart&auto=webp&s=a2388e77dfa5955cd0e2ad564b0540414850ba9e', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/RrY8l-dYD1MCHKGe9Rkf2zhnN28xpHx_AHZ2eh_nFAc.png?width=640&crop=smart&auto=webp&s=fb8c48fdd3808a565062e730309ff0ee1a20c36e', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/RrY8l-dYD1MCHKGe9Rkf2zhnN28xpHx_AHZ2eh_nFAc.png?width=960&crop=smart&auto=webp&s=df445fc5dd7e337e208feb2ab197de0fbe1b0b64', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/RrY8l-dYD1MCHKGe9Rkf2zhnN28xpHx_AHZ2eh_nFAc.png?width=1080&crop=smart&auto=webp&s=e41acbc9fb372060f4b02cfeb96d34499f4f67b7', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/RrY8l-dYD1MCHKGe9Rkf2zhnN28xpHx_AHZ2eh_nFAc.png?auto=webp&s=28892ff09e843930bad14469952c515ea258a297', 'width': 1200}, 'variants': {}}]} |
Context-based text classification: same header, different meanings - how to distinguish? | 0 | I have documents where the same header keyword appears in two different contexts:
**Type A (remove):** Header + descriptive findings only
**Type B (keep):** Header + descriptive findings + action words like "performed", "completed", "successful", "tolerated"
**Current approach:** Regex matches header, extracts text until next section.
**Problem:** Can't tell Type A from Type B by header alone.
**Question:** What's the simplest way to add context detection?
* Keyword search in following N lines?
* Simple binary classifier?
* Rule-based scoring?
Looking for lightweight solution. What's worked for similar "same label, different content" problems?" | 2025-10-06T17:19:59 | https://www.reddit.com/r/LocalLLaMA/comments/1nzp9ws/contextbased_text_classification_same_header/ | phoenixtactics | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzp9ws | false | null | t3_1nzp9ws | /r/LocalLLaMA/comments/1nzp9ws/contextbased_text_classification_same_header/ | false | false | self | 0 | null |
Granite4 Small-h 32b-A9b (Q4_K_M) at FULL 1M context window is using only 73GB of VRAM - Life is good! | 125 | This model seems to fit nicely on a single H100 or RTX Pro 6000. it’s great for high context RAG. This is the perfect model for my use case of models that call multiple tools in the same prompt while RAGing a bunch of knowledge bases. Might be our new daily driver for RAG use cases. If they add reasoning and vision then this is probably going to be everybody’s workhorse model. Great job big blue!!
- KV cache set to Q8_0
- Output tokens set to 131,072
- Num_ctx set to 1000000 (I know it’s supposed to be 1048576 but Ollama errors out at that value for some reason)
- Unsloth recommended settings for everything else.
- Seems to support and perform “native” tool calling as well as GPT-OSS.
- 70.88 response tokens/s
- Open WebUI as my front end client and Ollama 0.12.4 rc6 for inference
- FRIGGIN’ 1 Million context window locally is crazy to me!!
| 2025-10-06T17:09:38 | https://www.reddit.com/r/LocalLLaMA/comments/1nzozpg/granite4_smallh_32ba9b_q4_k_m_at_full_1m_context/ | Porespellar | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzozpg | false | null | t3_1nzozpg | /r/LocalLLaMA/comments/1nzozpg/granite4_smallh_32ba9b_q4_k_m_at_full_1m_context/ | false | false | self | 125 | null |
AI for Scientific Discovery is a Social Problem - so we made Hugging Science! | 97 | Hi all, I am Avijit Ghosh from Hugging Face. I wanted to share our new initiative for Scientific Discovery using Open source AI.
My colleague Georgia Channing and I just published a position paper that challenges a core assumption in AI for science: that the main barriers are technical.
They're not. We systematically analyzed why AI tools aren't democratizing scientific discovery and found that culture, incentives, and coordination failures are the real bottlenecks:
🚨 The "AI Scientist" myth is counterproductive: Waiting for AGI to solve science delays advances we could achieve now. Worse, it devalues human expertise essential for discovery and obscures science's real purpose: cultivating human understanding, not just producing outputs. (For eg: a transformer model achieves high accuracy predicting planetary motion but learns completely wrong physics.)
📊 We're rewarding the wrong contributions: High-quality datasets often have 100x longer impact than individual models, yet data curation work is systematically undervalued in hiring and tenure. Most models are superseded within months. Good datasets underpin research for decades.
⚠️ Collaboration is broken: Domain scientists prioritize mechanistic understanding. ML researchers optimize for predictive performance. Without shared language or success criteria, projects fail before they start. We lack educational resources for technically mature but domain-naive ML practitioners (and vice versa).
🔍 Accessibility and Fragmentation Remain Major Challenges: Harmonizing just 9 cancer imaging files took 329.5 hours over 6 months. Global South researchers face 6-day iteration cycles versus 30 minutes in G7 countries. 66% of scientists rate their computing access as inadequate. Current AI architectures struggle with complex scientific data that lacks clear tokenization strategies.
Why this matters now: While we chase narrow domain-specific applications, upstream computational bottlenecks like efficient PDE solvers and multi-scale coupling go unsolved. These problems could accelerate discovery across physics, chemistry, biology, and materials science simultaneously.
We need to build infrastructure, incentives, and community practices that make AI tools actually accessible.
That's why we're launching Hugging Science! A global community committed to addressing these barriers through concrete action: collaborative challenges targeting upstream problems, cross-disciplinary education and exchange, recognition for data and infrastructure contributions, and community-owned, accessible infrastructure.
This requires coordinated effort from researchers, funders, and institutions. But the foundation starts with community. Whether you curate datasets, build infrastructure, or bridge disciplines, there's a place for you!
Links:
Position Paper: https://huggingface.co/papers/2509.06580
Hugging Science Org: https://huggingface.co/hugging-science
Would love to know what you think and even better if you join the community and contribute! | 2025-10-06T17:08:39 | https://www.reddit.com/gallery/1nzoyu1 | evijit | reddit.com | 1970-01-01T00:00:00 | 0 | {} | 1nzoyu1 | false | null | t3_1nzoyu1 | /r/LocalLLaMA/comments/1nzoyu1/ai_for_scientific_discovery_is_a_social_problem/ | false | false | 97 | null | |
Which model for local text summarization? | 6 | Hi, I need a local model to transform webpages (like Wikipedia) into my markdown structure. Which model would you recommend for that? It will be 10.000s of pages but speed is not an issue. Running a 4090 i inherited from my late brother. | 2025-10-06T16:50:20 | https://www.reddit.com/r/LocalLLaMA/comments/1nzoh4i/which_model_for_local_text_summarization/ | roundshirt19 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzoh4i | false | null | t3_1nzoh4i | /r/LocalLLaMA/comments/1nzoh4i/which_model_for_local_text_summarization/ | false | false | self | 6 | null |
Looking for a cloud service to train GPT-2 like Andrej Karpathy, but I don’t have a credit card — any PayPal-friendly options? | 4 | Hi everyone,
I’m a beginner learning AI and I’m currently following Andrej Karpathy’s “build GPT from scratch” course. In his training demo, he used 8×H100 GPUs for 24 hours on Lambda Cloud.
I really want to try training a small GPT-2 model myself, but I don’t have a credit card, so I can’t use Lambda Cloud or most of the big providers.
Are there any good cloud GPU services where I can rent H100s (or something close) and pay via PayPal instead of a credit card?
Any suggestions or personal experiences would be super appreciated!
Thanks a lot in advance！ | 2025-10-06T16:33:47 | https://www.reddit.com/r/LocalLLaMA/comments/1nzo10s/looking_for_a_cloud_service_to_train_gpt2_like/ | HonestChampionship83 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzo10s | false | null | t3_1nzo10s | /r/LocalLLaMA/comments/1nzo10s/looking_for_a_cloud_service_to_train_gpt2_like/ | false | false | self | 4 | null |
LM Studio download cache location | 2 | How can I change the location where models are being downloaded? I mean in particular cache while it's downloading. It's saving into my E drive as I specified, but while downloading everything is going into my C drive which doesn't have enough space.
Any suggestions? | 2025-10-06T16:25:35 | https://www.reddit.com/r/LocalLLaMA/comments/1nznsx2/lm_studio_download_cache_location/ | HiveMate | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nznsx2 | false | null | t3_1nznsx2 | /r/LocalLLaMA/comments/1nznsx2/lm_studio_download_cache_location/ | false | false | self | 2 | null |
can I please get some pointers on constructing llama.cpp llama-server command tailored to VRAM+system RAM | 4 | I see many different results achieved by users by tailoring the llama.cpp server command to their system. ie how many layers to offload with -ngl and --n-cpu-moe etc. but if there are no similiar systems to take as a starting point is it just a case of trial by error?
For example if I wanted to run Qwen3-235B-A22B-Instruct-2507-UD-Q4\_K\_XL which is 135GB on a dual 3090 with 128GB system RAM, I wanted to figure out the best parameters for server command to maximise speed of the system response.
There have been times when using other peoples commands on what are identically specced systems to mine have resulted in failure to load the models, so its all a bit of a mystery to me still and regex still befuddles me. eg one user runs GPT-OSS-120B on a 2x3090 ad 96GB Ram using
[\--n-cpu-moe 15 --n-gpu-layers 999 --tensor-split 3,1.3 -c 131072 -fa on --jinja --reasoning-format none](https://www.reddit.com/r/LocalLLaMA/comments/1n61mm7/comment/nc99fji/?context=3)
To achieve 45 t/s. whereas when I try that llama-server errors out | 2025-10-06T16:05:21 | https://www.reddit.com/r/LocalLLaMA/comments/1nzn8w3/can_i_please_get_some_pointers_on_constructing/ | munkiemagik | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzn8w3 | false | null | t3_1nzn8w3 | /r/LocalLLaMA/comments/1nzn8w3/can_i_please_get_some_pointers_on_constructing/ | false | false | self | 4 | null |
Running GPT-OSS (OpenAI) Exclusively on AMD Ryzen™ AI NPU | 342 | We’re a small team building **FastFlowLM (FLM)** — a fast runtime for running **GPT-OSS, Gemma3 (vision), Medgemma,** **Qwen3,** **DeepSeek-R1**, **LLaMA3.x,** and others **entirely on the AMD Ryzen AI NPU**.
Think **Ollama**, but deeply optimized for AMD NPUs — with both **CLI** and **Server Mode (OpenAI-compatible)**.
✨ **From Idle Silicon to Instant Power — FastFlowLM (FLM) Makes Ryzen™ AI Shine.**
# Key Features
* No GPU fallback
* **Faster and over 10× more power efficient.**
* **Supports context lengths up to 256k tokens (qwen3:4b-2507).**
* **Ultra-Lightweight (14 MB). Installs within 20 seconds.**
# Try It Out
* **GitHub:** [github.com/FastFlowLM/FastFlowLM](https://github.com/FastFlowLM/FastFlowLM)
* **Live Demo → Remote machine access on the repo page**
* **YouTube Demos:** [FastFlowLM - YouTube](https://www.youtube.com/@FastFlowLM-YT/playlists) *→ Quick start guide, NPU vs CPU vs GPU, etc.*
We’re iterating fast and would **love your feedback, critiques, and ideas**🙏 | 2025-10-06T15:58:14 | https://youtu.be/ksYyiUQvYfo?si=zfBjb7U86P947OYW | BandEnvironmental834 | youtu.be | 1970-01-01T00:00:00 | 0 | {} | 1nzn1mk | false | {'oembed': {'author_name': 'FastFlowLM', 'author_url': 'https://www.youtube.com/@FastFlowLM-YT', 'height': 200, 'html': '<iframe width="267" height="200" src="https://www.youtube.com/embed/ksYyiUQvYfo?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="GPT-OSS-20B (OpenAI) — 100% Powered by AMD Ryzen™ AI NPU (First MoE model on NPUs)"></iframe>', 'provider_name': 'YouTube', 'provider_url': 'https://www.youtube.com/', 'thumbnail_height': 360, 'thumbnail_url': 'https://i.ytimg.com/vi/ksYyiUQvYfo/hqdefault.jpg', 'thumbnail_width': 480, 'title': 'GPT-OSS-20B (OpenAI) — 100% Powered by AMD Ryzen™ AI NPU (First MoE model on NPUs)', 'type': 'video', 'version': '1.0', 'width': 267}, 'type': 'youtube.com'} | t3_1nzn1mk | /r/LocalLLaMA/comments/1nzn1mk/running_gptoss_openai_exclusively_on_amd_ryzen_ai/ | false | false | default | 342 | {'enabled': False, 'images': [{'id': '_S9eclPc4WRscWHOsVO80UpXnpu4dfbG_wCYpnVLuPA', 'resolutions': [{'height': 81, 'url': 'https://external-preview.redd.it/_S9eclPc4WRscWHOsVO80UpXnpu4dfbG_wCYpnVLuPA.jpeg?width=108&crop=smart&auto=webp&s=73407d41a297a320b9c9bed5919e5eeef67d314b', 'width': 108}, {'height': 162, 'url': 'https://external-preview.redd.it/_S9eclPc4WRscWHOsVO80UpXnpu4dfbG_wCYpnVLuPA.jpeg?width=216&crop=smart&auto=webp&s=b49d3c87d82fdd0ca6573a0d26371c8795d916af', 'width': 216}, {'height': 240, 'url': 'https://external-preview.redd.it/_S9eclPc4WRscWHOsVO80UpXnpu4dfbG_wCYpnVLuPA.jpeg?width=320&crop=smart&auto=webp&s=d53f3fcd941a63a8ecb5a50a6c26e1cf55db3e1a', 'width': 320}], 'source': {'height': 360, 'url': 'https://external-preview.redd.it/_S9eclPc4WRscWHOsVO80UpXnpu4dfbG_wCYpnVLuPA.jpeg?auto=webp&s=8f1784e85dd5e549db19d0d9879e8fd99c814300', 'width': 480}, 'variants': {}}]} |
Mini AI Project Help | 1 | [removed] | 2025-10-06T15:57:52 | https://www.reddit.com/r/LocalLLaMA/comments/1nzn1a7/mini_ai_project_help/ | Ok_Television_9000 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzn1a7 | false | null | t3_1nzn1a7 | /r/LocalLLaMA/comments/1nzn1a7/mini_ai_project_help/ | false | false | self | 1 | null |
[Willing to Pay] Mini AI Project | 1 | [removed] | 2025-10-06T15:54:41 | https://www.reddit.com/r/LocalLLaMA/comments/1nzmy5u/willing_to_pay_mini_ai_project/ | Ok_Television_9000 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzmy5u | false | null | t3_1nzmy5u | /r/LocalLLaMA/comments/1nzmy5u/willing_to_pay_mini_ai_project/ | false | false | self | 1 | null |
MUSICA FIM DE SEMANA | 0 | Esse vídeo refere-se a apresentação da música sem fins. Lucrativos, apenas apresentação. | 2025-10-06T15:49:09 | https://www.reddit.com/r/LocalLLaMA/comments/1nzmsm0/musica_fim_de_semana/ | Bubbly-Average2634 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzmsm0 | false | null | t3_1nzmsm0 | /r/LocalLLaMA/comments/1nzmsm0/musica_fim_de_semana/ | false | false | self | 0 | null |
One-Click Installer Index-TTS2 works, but how to start for 2nd time ? | 0 | Hi,
i just tested the One-Click Installer for Index-TTS2 and it downloads everything and works, opens te site to use. After i close everything, how do i start the Index-TTS2 localy again? Or should i do the one-click install all over again every time? | 2025-10-06T15:16:57 | https://www.reddit.com/r/LocalLLaMA/comments/1nzlwe3/oneclick_installer_indextts2_works_but_how_to/ | WrapNeither | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzlwe3 | false | null | t3_1nzlwe3 | /r/LocalLLaMA/comments/1nzlwe3/oneclick_installer_indextts2_works_but_how_to/ | false | false | self | 0 | null |
Is WAN2.5 basically a VEO3 alternative? | 2 | [https://medium.com/@social\_18794/the-next-step-in-ai-video-meet-wan-2-5-f67ea7ff590e](https://medium.com/@social_18794/the-next-step-in-ai-video-meet-wan-2-5-f67ea7ff590e) | 2025-10-06T15:13:21 | https://www.reddit.com/r/LocalLLaMA/comments/1nzlssk/is_wan25_basically_a_veo3_alternative/ | Some-Cow-3692 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzlssk | false | null | t3_1nzlssk | /r/LocalLLaMA/comments/1nzlssk/is_wan25_basically_a_veo3_alternative/ | false | false | self | 2 | {'enabled': True, 'images': [{'id': '730Cdr9mdxuf9WTF2Dp8KAzkukPmV0cRY_-2OcwVqwg', 'resolutions': [{'height': 60, 'url': 'https://external-preview.redd.it/730Cdr9mdxuf9WTF2Dp8KAzkukPmV0cRY_-2OcwVqwg.gif?width=108&crop=smart&format=png8&s=54e3e491b9b8c86ff0e536248ea45194c3eab2b3', 'width': 108}, {'height': 121, 'url': 'https://external-preview.redd.it/730Cdr9mdxuf9WTF2Dp8KAzkukPmV0cRY_-2OcwVqwg.gif?width=216&crop=smart&format=png8&s=530d50888a3d755e2d8206fe7859bdbdddadf7a5', 'width': 216}, {'height': 180, 'url': 'https://external-preview.redd.it/730Cdr9mdxuf9WTF2Dp8KAzkukPmV0cRY_-2OcwVqwg.gif?width=320&crop=smart&format=png8&s=88b3daaae9debcf76ffcbd58f826e77647488e2e', 'width': 320}, {'height': 360, 'url': 'https://external-preview.redd.it/730Cdr9mdxuf9WTF2Dp8KAzkukPmV0cRY_-2OcwVqwg.gif?width=640&crop=smart&format=png8&s=fb4d5b3c6b3c255bf644b5bfebd667f6900df061', 'width': 640}], 'source': {'height': 450, 'url': 'https://external-preview.redd.it/730Cdr9mdxuf9WTF2Dp8KAzkukPmV0cRY_-2OcwVqwg.gif?format=png8&s=6af41e2421fa0fa086e262c78da53b7e21daed5f', 'width': 800}, 'variants': {'gif': {'resolutions': [{'height': 60, 'url': 'https://external-preview.redd.it/730Cdr9mdxuf9WTF2Dp8KAzkukPmV0cRY_-2OcwVqwg.gif?width=108&crop=smart&s=a4009b45e417a46f535697687d48a1a98281e474', 'width': 108}, {'height': 121, 'url': 'https://external-preview.redd.it/730Cdr9mdxuf9WTF2Dp8KAzkukPmV0cRY_-2OcwVqwg.gif?width=216&crop=smart&s=2091805560f14549660038482d8e42d47bed32ed', 'width': 216}, {'height': 180, 'url': 'https://external-preview.redd.it/730Cdr9mdxuf9WTF2Dp8KAzkukPmV0cRY_-2OcwVqwg.gif?width=320&crop=smart&s=3690c0a54f7052baf647d879711e8e4f1e4b7a6c', 'width': 320}, {'height': 360, 'url': 'https://external-preview.redd.it/730Cdr9mdxuf9WTF2Dp8KAzkukPmV0cRY_-2OcwVqwg.gif?width=640&crop=smart&s=8029ac9873f6b2f5af5514d6ada25fd44cc5d370', 'width': 640}], 'source': {'height': 450, 'url': 'https://external-preview.redd.it/730Cdr9mdxuf9WTF2Dp8KAzkukPmV0cRY_-2OcwVqwg.gif?s=d90f0f8dadde721a4eb8af97c237e83944331ad5', 'width': 800}}, 'mp4': {'resolutions': [{'height': 60, 'url': 'https://external-preview.redd.it/730Cdr9mdxuf9WTF2Dp8KAzkukPmV0cRY_-2OcwVqwg.gif?width=108&format=mp4&s=be9611ee891dfb63b726627cd33802ac3c6d6a1d', 'width': 108}, {'height': 121, 'url': 'https://external-preview.redd.it/730Cdr9mdxuf9WTF2Dp8KAzkukPmV0cRY_-2OcwVqwg.gif?width=216&format=mp4&s=eabac4b2f6ac7a0b00917f61cb75a79ef2fdaec5', 'width': 216}, {'height': 180, 'url': 'https://external-preview.redd.it/730Cdr9mdxuf9WTF2Dp8KAzkukPmV0cRY_-2OcwVqwg.gif?width=320&format=mp4&s=cad66e26fc528df82c05a98e4f9e03c3397ceada', 'width': 320}, {'height': 360, 'url': 'https://external-preview.redd.it/730Cdr9mdxuf9WTF2Dp8KAzkukPmV0cRY_-2OcwVqwg.gif?width=640&format=mp4&s=065b9b83f668a6d30259ad62603e370a9564810f', 'width': 640}], 'source': {'height': 450, 'url': 'https://external-preview.redd.it/730Cdr9mdxuf9WTF2Dp8KAzkukPmV0cRY_-2OcwVqwg.gif?format=mp4&s=d31ee133fbd078b966488a92e6132ee62a680677', 'width': 800}}}}]} |
How Transformers avoids becoming a black box, even at 1M+ LOC | 293 | Hello, I'm Pablo from Hugging Face Open-Source team. We just wrote a software-engineering focused deep dive on how we keep the \`transformers\` library hackable/maintainable while it keeps growing and growing. If you're running models locally, fine-tuning on your own hardware, or just want to understand the code you're using, I recommend the read!
Light spoilers about what's in it:
\- \*\*\*\*One Model, One File:\*\*\*\* You can still read a \`modeling\_\*.py\` top-to-bottom and see exactly what's happening.
\- \*\*\*\*Modular Transformers:\*\*\*\* This is our trick to fight code bloat. Contributors can reuse code via a small \`modular\_\*.py\` file, but we auto-generate the full, readable modeling file so you never lose the "one file" experience. It cut our maintenance work by \~15x.
\- \*\*\*\*Config-Driven Performance:\*\*\*\* Features like FlashAttention(and ofc 2,3..), tensor parallelism (\`tp\_plan\`), and per-layer attention schedules are enabled in the config, not by changing the model code. A \`Linear\` layer is always just a \`Linear\` layer, you don't have to change it depending on how it's sliced.
\- \*\*\*\*Tools for Local Use:\*\*\*\* This philosophy lets us build helpful tools. The post covers an attention visualizer, a model tracer for debugging ports, and faster CUDA warmups, and we also go over \`transformers serve\` usage.
Hope you enjoy the read! | 2025-10-06T14:53:04 | https://huggingface.co/spaces/transformers-community/Transformers-tenets | El_Olbap | huggingface.co | 1970-01-01T00:00:00 | 0 | {} | 1nzl8y5 | false | null | t3_1nzl8y5 | /r/LocalLLaMA/comments/1nzl8y5/how_transformers_avoids_becoming_a_black_box_even/ | false | false | default | 293 | null |
is the Threadripper PRO 9975WX (32Core) sufficient for a system with four NVIDIA RTX 6000 Pro GPUs? | 1 | Hi Community, I'm a bit confused on optimal spec choice for a ai inference system, and also hopefully correct place to post this.
Primary use is to use wan2.2 T2V and 128 Concurrent Users in a design community environment, my mind is overloaded trying to figure this out, so though to reach out to people more knowledgeable than me.
is there any reason to go higher CPU spec like 64 or 96 core? and is Ram to vram spec? still a bit confused as to ram to vram too.
Main System specs: TR Pro 9975wx, Asus WRX90E-Sage, V-color (6400) 8 x 48GB, 4 x RTX Pro 6000. | 2025-10-06T14:49:38 | https://www.reddit.com/r/LocalLLaMA/comments/1nzl5rk/is_the_threadripper_pro_9975wx_32core_sufficient/ | Jaded_Combination_25 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzl5rk | false | null | t3_1nzl5rk | /r/LocalLLaMA/comments/1nzl5rk/is_the_threadripper_pro_9975wx_32core_sufficient/ | false | false | self | 1 | null |
Batch inference with whisper.cpp | 1 | Recently, I used whisper.cpp repo to support my project, using STT task. However, When using segment model ( pyannote/segment3.0), audio is splited into subaudioas. Hence, whisper executes segment by segment is take long time. So, how to operate whisper with batch size. Or smart sollution. Help me please 🥺🥺.
Thank you so much | 2025-10-06T14:34:28 | https://www.reddit.com/r/LocalLLaMA/comments/1nzkra5/batch_inference_with_whispercpp/ | baduyne | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzkra5 | false | null | t3_1nzkra5 | /r/LocalLLaMA/comments/1nzkra5/batch_inference_with_whispercpp/ | false | false | self | 1 | null |
How to add a local LLM in a Slicer 3D program? They're open source projects | 4 | Hey guys, I just bought a 3D printer and I'm learning by doing all the configuration to set in my slicer (Flsun slicer) and I came up with the idea to have a llm locally and create a "copilot" for the slicer to help explaining all the varius stuff and also to adjust the settings, depending on the model. So I found ollama and just starting. Can you help me with any type of advices? Every help is welcome | 2025-10-06T14:12:02 | https://www.reddit.com/r/LocalLLaMA/comments/1nzk5z3/how_to_add_a_local_llm_in_a_slicer_3d_program/ | alex_studiolab | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzk5z3 | false | null | t3_1nzk5z3 | /r/LocalLLaMA/comments/1nzk5z3/how_to_add_a_local_llm_in_a_slicer_3d_program/ | false | false | self | 4 | null |
Connected a 3090 to my Strix Halo | 56 | 2025-10-06T14:10:06 | https://www.reddit.com/r/LocalLLaMA/comments/1nzk46z/connected_a_3090_to_my_strix_halo/ | itsjustmarky | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzk46z | false | null | t3_1nzk46z | /r/LocalLLaMA/comments/1nzk46z/connected_a_3090_to_my_strix_halo/ | false | false | 56 | null | ||
October 2025 model selections, what do you use? | 173 | 2025-10-06T13:10:24 | getpodapp | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1nzimvg | false | null | t3_1nzimvg | /r/LocalLLaMA/comments/1nzimvg/october_2025_model_selections_what_do_you_use/ | false | false | default | 173 | {'enabled': True, 'images': [{'id': 'syzg3f8oqhtf1', 'resolutions': [{'height': 44, 'url': 'https://preview.redd.it/syzg3f8oqhtf1.png?width=108&crop=smart&auto=webp&s=1cb6473ba40bc13f66ab4aa20c21caf36d2085ac', 'width': 108}, {'height': 88, 'url': 'https://preview.redd.it/syzg3f8oqhtf1.png?width=216&crop=smart&auto=webp&s=ffaab50b7989c68d246f6ee6d3a4de17198d4b8f', 'width': 216}, {'height': 130, 'url': 'https://preview.redd.it/syzg3f8oqhtf1.png?width=320&crop=smart&auto=webp&s=aa689d90cb88df3c65e95e831ec0cef3e98acd4d', 'width': 320}, {'height': 261, 'url': 'https://preview.redd.it/syzg3f8oqhtf1.png?width=640&crop=smart&auto=webp&s=e686e4b8db47ba995e1c43c3c24fb0dd3547175e', 'width': 640}, {'height': 392, 'url': 'https://preview.redd.it/syzg3f8oqhtf1.png?width=960&crop=smart&auto=webp&s=5b8bda39ca9599d5f0aab7188477bb40808a37b7', 'width': 960}, {'height': 442, 'url': 'https://preview.redd.it/syzg3f8oqhtf1.png?width=1080&crop=smart&auto=webp&s=1913cfa193045f4e87f9b5890dda4b1f35e802d3', 'width': 1080}], 'source': {'height': 564, 'url': 'https://preview.redd.it/syzg3f8oqhtf1.png?auto=webp&s=325d6b25173a7968ab3f21b5b1bb80e2e0c1fecb', 'width': 1378}, 'variants': {}}]} | ||
What's the best local LLM for coding I can run on MacBook Pro M4 32Gb? | 11 | I have two macbook pros, one is a 14" MBP with M4 and 32Gb and the other is a 16" M4 Pro with 48Gb
I wanted to know what is the best one I can run locally that has reasonable even if slightly slow, I assume the extra core count and RAM would help the bigger.
So far I've tried **qwen2.5-coder:3b** for autocompletion which is mostly OK, and **deepseek-r1:14b** for the chat/agent in the M4 32Gb one and it works but it's slower than what I would like it to be... Is there any model that performs the same/better and that is also faster even if it's a little bit? | 2025-10-06T13:09:30 | https://www.reddit.com/r/LocalLLaMA/comments/1nzim4o/whats_the_best_local_llm_for_coding_i_can_run_on/ | SuperShittyShot | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzim4o | false | null | t3_1nzim4o | /r/LocalLLaMA/comments/1nzim4o/whats_the_best_local_llm_for_coding_i_can_run_on/ | false | false | self | 11 | null |
TransFire: an app/tool to chat with your local LLMs while far from home, without port forwarding and with AES encryption | 14 | I recently released a quick project that I did this week to chat with my local models while avoiding the hassle of configuring port forwarding.
Here is the result: [https://github.com/Belluxx/TransFire](https://github.com/Belluxx/TransFire)
It comes with an Android app and a python script. The app allows you to chat with the model, while the script acts as a bridge/server between the app and the computer that is running the LLMs.
It uses a free Firebase instance as intermediary and encrypts all traffic with AES.
You will need to create your own firebase project to use TransFire.
https://preview.redd.it/i9m8ud9jnhtf1.png?width=1080&format=png&auto=webp&s=867de789e4ffa319fe3152e3f31673c0f95848c9
| 2025-10-06T12:54:21 | https://www.reddit.com/r/LocalLLaMA/comments/1nzi956/transfire_an_apptool_to_chat_with_your_local_llms/ | EntropyMagnets | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzi956 | false | null | t3_1nzi956 | /r/LocalLLaMA/comments/1nzi956/transfire_an_apptool_to_chat_with_your_local_llms/ | false | false | 14 | {'enabled': False, 'images': [{'id': 'P5OQQK2kdb9BIFrZ4yGjMVBV2XzxCl3t0FXq2tGm9l4', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/P5OQQK2kdb9BIFrZ4yGjMVBV2XzxCl3t0FXq2tGm9l4.png?width=108&crop=smart&auto=webp&s=dea7518cdd1abafc1dca3462585c8f4d0206204c', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/P5OQQK2kdb9BIFrZ4yGjMVBV2XzxCl3t0FXq2tGm9l4.png?width=216&crop=smart&auto=webp&s=06e0b254659b3788281ce9d7a0412b23f44faf98', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/P5OQQK2kdb9BIFrZ4yGjMVBV2XzxCl3t0FXq2tGm9l4.png?width=320&crop=smart&auto=webp&s=5de178015669f85f19c2b2af7af0a141d8d3a186', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/P5OQQK2kdb9BIFrZ4yGjMVBV2XzxCl3t0FXq2tGm9l4.png?width=640&crop=smart&auto=webp&s=4c21a019f1a5a8e89f0688ba5cbf120121d6e931', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/P5OQQK2kdb9BIFrZ4yGjMVBV2XzxCl3t0FXq2tGm9l4.png?width=960&crop=smart&auto=webp&s=e83339a8a849ca62f51ba6df7f5e1412334731a1', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/P5OQQK2kdb9BIFrZ4yGjMVBV2XzxCl3t0FXq2tGm9l4.png?width=1080&crop=smart&auto=webp&s=e33f6e26d0add156c668051b71a5cae3a6a66df1', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/P5OQQK2kdb9BIFrZ4yGjMVBV2XzxCl3t0FXq2tGm9l4.png?auto=webp&s=5fd212e0b1a9085fd35287f8f495ff879180fcea', 'width': 1200}, 'variants': {}}]} | |
Is agentic programming on own HW actually feasible? | 31 | Being a senior dev I gotta admit that latest models are really good, yes it's still not "job replacing" good, but they are surprisingly capable (I am talking mostly about Claude 4.5 and similar). I was making some simple calculations and it seems to me that these agentic tools that they are selling now are almost impossible to return any profit to them with current prices, it seems like they just pushed the prices as low as possible to onboard all possible enterprise customers and get them totally dependent on their AI services before dramatically increasing the price, so I am assuming all these are available just temporarily.
So yes, agentic programming on those massive GPU farms with hundreds of thousand GPUs look like it work great, because it writes a lot of output very fast (1000TPS+), but since you can't rely on this stuff being "almost free" forever, I am wondering: Is running similar models locally to get any real work done actually feasible?
I have a rather low-end HW for AI (16GB VRAM on RTX 4060Ti + 64 GB DDR4 on mobo) and best models I could get to run were < 24b models with quantization or higher parameter models using DMA to motherboard (which resulted in inference being about 10x slower, but it gave me an idea what I would be able to get with slightly more VRAM).
Smaller models are IMHO absolutely unusable. They just can't get any real or useful work done. For stuff similar to Claude you probably need something like deepseek or llama full with FP16, that's like 671b parameters, so what kind of VRAM you need for that? 512GB is probably minimum if you run some kind of quantization (dumbing the model down). If you want some decent context window too, that's like 1TB VRAM?
Then how fast is that going to be, if you get something like Apple Studio with shared RAM between CPU and GPU? What TPS you get? 5? 10? Maybe even less?
I think with that speed, you don't only have to spend ENORMOUS money upfront, but you end up with something that will need 2 hours to solve something you could do by yourself in 1 hour.
Sure you can keep it running when you are sleeping working over night, but then you still have to pay electricity right? We talk about system that could easily have 1, maybe 2kW input at that size?
Or maybe my math is totally off? IDK, is there anyone that actually does it and built a system that can run top models and get agentic programming work done on similar level of quality you get from Claude 4.5 or codex? How much did it cost to buy? How fast is it? | 2025-10-06T12:38:03 | https://www.reddit.com/r/LocalLLaMA/comments/1nzhvoo/is_agentic_programming_on_own_hw_actually_feasible/ | petr_bena | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzhvoo | false | null | t3_1nzhvoo | /r/LocalLLaMA/comments/1nzhvoo/is_agentic_programming_on_own_hw_actually_feasible/ | false | false | self | 31 | null |
Survey: Challenges in Evaluating AI Agents (Especially Multi-Turn) | 0 | Hey everyone!
We, at Innowhyte, have been developing AI agents using an evaluation-driven approach. Through this work, we've encountered various evaluation challenges and created internal tools to address them. We'd like to connect with the community to see if others face similar challenges or have encountered issues we haven't considered yet.
If you have 10 mins, please fill out the form below to provide your responses:
[https://forms.gle/hVK3AkJ4uaBya8u9A](https://forms.gle/hVK3AkJ4uaBya8u9A)
If you do not have the time, you can also add your challenges as comments!
PS: Filling the form would be better, that way I can filter out bots :D | 2025-10-06T12:24:45 | https://www.reddit.com/r/LocalLLaMA/comments/1nzhl1f/survey_challenges_in_evaluating_ai_agents/ | shivmohith8 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzhl1f | false | null | t3_1nzhl1f | /r/LocalLLaMA/comments/1nzhl1f/survey_challenges_in_evaluating_ai_agents/ | false | false | self | 0 | {'enabled': False, 'images': [{'id': 'itF52iZtij0CRQ7gF3J69R4vWs3ycW5mBaqVXSWQ6zI', 'resolutions': [{'height': 56, 'url': 'https://external-preview.redd.it/itF52iZtij0CRQ7gF3J69R4vWs3ycW5mBaqVXSWQ6zI.png?width=108&crop=smart&auto=webp&s=9c0bd9d36a7a6088eaeb0e5797a918a244a6e6a0', 'width': 108}, {'height': 113, 'url': 'https://external-preview.redd.it/itF52iZtij0CRQ7gF3J69R4vWs3ycW5mBaqVXSWQ6zI.png?width=216&crop=smart&auto=webp&s=60bd2505538505c8f708122c76aa2ccc6a3ef4eb', 'width': 216}, {'height': 168, 'url': 'https://external-preview.redd.it/itF52iZtij0CRQ7gF3J69R4vWs3ycW5mBaqVXSWQ6zI.png?width=320&crop=smart&auto=webp&s=eaceea1b919b2eae4b606560db52ea117beaa90a', 'width': 320}, {'height': 336, 'url': 'https://external-preview.redd.it/itF52iZtij0CRQ7gF3J69R4vWs3ycW5mBaqVXSWQ6zI.png?width=640&crop=smart&auto=webp&s=96546c601824efeb7cac4fecb2f04d2815c557f1', 'width': 640}, {'height': 504, 'url': 'https://external-preview.redd.it/itF52iZtij0CRQ7gF3J69R4vWs3ycW5mBaqVXSWQ6zI.png?width=960&crop=smart&auto=webp&s=2d2b0a79f9e4227eea9350c671d5adce68288a71', 'width': 960}, {'height': 567, 'url': 'https://external-preview.redd.it/itF52iZtij0CRQ7gF3J69R4vWs3ycW5mBaqVXSWQ6zI.png?width=1080&crop=smart&auto=webp&s=4316f3b8c8e7458915dffd014d79d24917e57b4e', 'width': 1080}], 'source': {'height': 630, 'url': 'https://external-preview.redd.it/itF52iZtij0CRQ7gF3J69R4vWs3ycW5mBaqVXSWQ6zI.png?auto=webp&s=8578df0955b8a70ed2826e3c893d1a41e0c9c498', 'width': 1200}, 'variants': {}}]} |
Survey: Challenges in Evaluating AI Agents (Especially Multi-Turn) | 0 | Hey everyone!
We, at Innowhyte, have been developing AI agents using an evaluation-driven approach. Through this work, we've encountered various evaluation challenges and created internal tools to address them. We'd like to connect with the community to see if others face similar challenges or have encountered issues we haven't considered yet.
If you have 10 mins, please fill out the form below to provide your responses:
[https://forms.gle/hVK3AkJ4uaBya8u9A](https://forms.gle/hVK3AkJ4uaBya8u9A)
If you do not have the time, you can also add your challenges as comments!
PS: Filling the form would be better, that way I can filter out bots :D | 2025-10-06T12:22:37 | https://www.reddit.com/r/LocalLLaMA/comments/1nzhjei/survey_challenges_in_evaluating_ai_agents/ | shivmohith8 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzhjei | false | null | t3_1nzhjei | /r/LocalLLaMA/comments/1nzhjei/survey_challenges_in_evaluating_ai_agents/ | false | false | self | 0 | {'enabled': False, 'images': [{'id': 'itF52iZtij0CRQ7gF3J69R4vWs3ycW5mBaqVXSWQ6zI', 'resolutions': [{'height': 56, 'url': 'https://external-preview.redd.it/itF52iZtij0CRQ7gF3J69R4vWs3ycW5mBaqVXSWQ6zI.png?width=108&crop=smart&auto=webp&s=9c0bd9d36a7a6088eaeb0e5797a918a244a6e6a0', 'width': 108}, {'height': 113, 'url': 'https://external-preview.redd.it/itF52iZtij0CRQ7gF3J69R4vWs3ycW5mBaqVXSWQ6zI.png?width=216&crop=smart&auto=webp&s=60bd2505538505c8f708122c76aa2ccc6a3ef4eb', 'width': 216}, {'height': 168, 'url': 'https://external-preview.redd.it/itF52iZtij0CRQ7gF3J69R4vWs3ycW5mBaqVXSWQ6zI.png?width=320&crop=smart&auto=webp&s=eaceea1b919b2eae4b606560db52ea117beaa90a', 'width': 320}, {'height': 336, 'url': 'https://external-preview.redd.it/itF52iZtij0CRQ7gF3J69R4vWs3ycW5mBaqVXSWQ6zI.png?width=640&crop=smart&auto=webp&s=96546c601824efeb7cac4fecb2f04d2815c557f1', 'width': 640}, {'height': 504, 'url': 'https://external-preview.redd.it/itF52iZtij0CRQ7gF3J69R4vWs3ycW5mBaqVXSWQ6zI.png?width=960&crop=smart&auto=webp&s=2d2b0a79f9e4227eea9350c671d5adce68288a71', 'width': 960}, {'height': 567, 'url': 'https://external-preview.redd.it/itF52iZtij0CRQ7gF3J69R4vWs3ycW5mBaqVXSWQ6zI.png?width=1080&crop=smart&auto=webp&s=4316f3b8c8e7458915dffd014d79d24917e57b4e', 'width': 1080}], 'source': {'height': 630, 'url': 'https://external-preview.redd.it/itF52iZtij0CRQ7gF3J69R4vWs3ycW5mBaqVXSWQ6zI.png?auto=webp&s=8578df0955b8a70ed2826e3c893d1a41e0c9c498', 'width': 1200}, 'variants': {}}]} |
Local Model Recs 12B-24B - Suitable for 3rd-person story-writing. | 0 | After messing with local models from huggingface for a few months, I've realized there is zero standardization for anything regarding style. "Roleplay" means something different to every person, and the styles that fine-tunes are trained on can be really weird, like 2nd-person present tense. \*shudders\*
I'm also hoping to find something that's actually trained on novels or literotica. Not to dump on any of the model tuners out there, but seeing something like this is a \*huge\* red flag for me:
>How It Was Made
>\[Redacted\] text adventure data was generated by simulating playthroughs of published character creator scenarios from AI Dungeon. Five distinct user archetypes played through each scenario, whose character starts all varied in faction, location, etc. to generate five unique samples.
>One language model played the role of narrator, with the other playing the user. They were blind to each other’s underlying logic, so the user was actually capable of surprising the narrator with their choices. Each simulation was allowed to run for 8k tokens or until the main character died.
>\[Redacted\]'s general emotional sentiment is one of pessimism, where failure is frequent and plot armor does not exist for anyone. This serves to counter the positivity bias so inherent in our language models nowadays.
I'm looking for something that has real effort and human-generated writing used, not recycled AI slop. Preferably something that can crank out 800-1000 token novel-like messages and actually be \*geared\* for that.
Any suggestions? (Also the 24B limit can be theoretically increased to whatever will fit well in 16GB VRAM, but it will have to be \*really\* good for me to consider dropping below 16k context.) | 2025-10-06T12:07:53 | https://www.reddit.com/r/LocalLLaMA/comments/1nzh86m/local_model_recs_12b24b_suitable_for_3rdperson/ | Zathura2 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzh86m | false | null | t3_1nzh86m | /r/LocalLLaMA/comments/1nzh86m/local_model_recs_12b24b_suitable_for_3rdperson/ | false | false | self | 0 | null |
How to run LLMs on a 1GB (e-waste) GPU without changing a single line of code | 17 | Accelera is working at some scale. And you 𝐝𝐨 𝐧𝐨𝐭 𝐡𝐚𝐯𝐞 𝐭𝐨 𝐫𝐞𝐜𝐨𝐦𝐩𝐢𝐥𝐞 𝐨𝐫 𝐦𝐨𝐝𝐢𝐟𝐲 𝐚 𝐬𝐢𝐧𝐠𝐥𝐞 𝐥𝐢𝐧𝐞 𝐨𝐟 𝐲𝐨𝐮𝐫 𝐜𝐨𝐝𝐞𝐛𝐚𝐬𝐞.
I was facing an odd problem over quite a few years now, and that is I am quite poor, and I can not do anything about it for so long. I work hard, take the next step, but somehow the new base set, and I am stuck there again. And this also makes me GPU poor. I can not even load the whole wan models in my GPU. But I have some specific skillset, and one of them is designing the most weirdest algorithm, but they work, and they also scale. So here is what I did. I have enough RAM to keep loading the weights on demand and transfer them onto GPU, perform the operation on GPU and return back to CPU, and keep doing this till we are done. This way I was able limit the usage VRAM load so much that max hit 400 megabytes, not even a gigabytes.
So now we can run wan on 16gb machine with mobile GPU of less than 1gb VRAM, so it fits the description of everyday developer laptop. This is not just a moment for me, but for us. Think about how much e-waste we can make reusable with this. Think about how many clusters we can make just by integrating them with accelera, definetly they will be slower than latest cutting edge devices, but it is one more fighting chances to lacking startups or indie developers.
Right now I am trying to make it distributed to multiple device and parallel weight loading. And I am pretty sure it will be a quite turbulent path, but I will definetly explore it, and resolve it.
This is just a technique to intercept pytorch method and replace their with my efficient matmul code. It also makes me limited, if something is not implemented in torch, it simply can not optimize it. But on the bright side, we can use this without any recompile or modification of the codebase.
Please share your thoughts and suggestions. Today (2025.10.06) the video is jittery, but it will not be for very long.
Source code: [https://github.com/maifeeulasad/Accelera/](https://github.com/maifeeulasad/Accelera/)
PIP package: [https://pypi.org/project/accelera/](https://pypi.org/project/accelera/) | 2025-10-06T11:42:30 | https://www.reddit.com/r/LocalLLaMA/comments/1nzgp8q/how_to_run_llms_on_a_1gb_ewaste_gpu_without/ | maifee | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzgp8q | false | null | t3_1nzgp8q | /r/LocalLLaMA/comments/1nzgp8q/how_to_run_llms_on_a_1gb_ewaste_gpu_without/ | false | false | self | 17 | {'enabled': False, 'images': [{'id': 'a4PpwMPY_kKxNzBaShK3TOOAgjrnAj0TRIX3zOX84Uc', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/a4PpwMPY_kKxNzBaShK3TOOAgjrnAj0TRIX3zOX84Uc.png?width=108&crop=smart&auto=webp&s=6d04d4a69657f14952c2c484750184d0e527eb2a', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/a4PpwMPY_kKxNzBaShK3TOOAgjrnAj0TRIX3zOX84Uc.png?width=216&crop=smart&auto=webp&s=48365e6201dd1b66a8a85bb321e1e5eec0cb78b7', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/a4PpwMPY_kKxNzBaShK3TOOAgjrnAj0TRIX3zOX84Uc.png?width=320&crop=smart&auto=webp&s=e3bccb4b1b2b8b3abefa5a743bd623efd1fb059b', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/a4PpwMPY_kKxNzBaShK3TOOAgjrnAj0TRIX3zOX84Uc.png?width=640&crop=smart&auto=webp&s=8477e8b45b65f4f50aec4c5986ea2f697f3634dc', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/a4PpwMPY_kKxNzBaShK3TOOAgjrnAj0TRIX3zOX84Uc.png?width=960&crop=smart&auto=webp&s=20c09690a730f2d70681e6bf0f3baf18c6d5b20c', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/a4PpwMPY_kKxNzBaShK3TOOAgjrnAj0TRIX3zOX84Uc.png?width=1080&crop=smart&auto=webp&s=89890c24e42ee30c4478f051215c5cc84e9ee635', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/a4PpwMPY_kKxNzBaShK3TOOAgjrnAj0TRIX3zOX84Uc.png?auto=webp&s=dd70de4263e1ac8e53a6b84a96965c8a59c168a4', 'width': 1200}, 'variants': {}}]} |
Renting AI Servers for +50B LLM Fine-Tuning/Inference – Need Hardware, Cost, and Security Advice! | 7 | Like many hobbyists/indie developers, buying a multi-GPU server to handle the latest monster LLMs is just not financially viable for me right now. I'm looking to rent cloud GPU compute to work with large open-source models (specifically in the 50B-70B+ parameter range) for both fine-tuning (LoRA) and inference.
My budget isn't unlimited, and I'm trying to figure out the most cost-effective path without completely sacrificing performance.
I'm hitting a wall on three main points and would love to hear from anyone who has successfully done this:
1. The Hardware Sweet Spot for +50B Models
The consensus seems to be that I'll need a lot of VRAM, likely partitioned across multiple GPUs. Given that I'm aiming for the $50B+ range:
What is the minimum aggregate VRAM I should be looking for? Is ∼80GB−100GB for a quantized model realistic, or should I aim higher?
Which specific GPUs are the current cost-performance kings for this size? I see a lot of talk about A100s, H100s, and even clusters of high-end consumer cards (e.g., RTX 5090/4090s with modded VRAM). Which is the most realistic to find and rent affordably on platforms like RunPod, [Vast.ai](http://Vast.ai), CoreWeave, or Lambda Labs?
Is an 8-bit or 4-bit quantization model a must for this size when renting?
2. Cost Analysis: Rental vs. API
I'm trying to prove a use-case where renting is more cost-effective than just using a commercial API (like GPT-4, Claude, etc.) for high-volume inference/fine-tuning.
For someone doing an initial fine-tuning run, what's a typical hourly cost range I should expect for a cluster of sufficient GPUs (e.g., 4x A100 40GB or similar)?
What hidden costs should I watch out for? (Storage fees, networking egress, idle time, etc.)
3. The Big Worry: Cloud Security (Specifically Multi-Tenant)
My data (both training data and the resulting fine-tuned weights/model) is sensitive. I'm concerned about the security of running these workloads on multi-tenant, shared-hardware cloud providers.
How real is the risk of a 'side-channel attack' or 'cross-tenant access' to my VRAM/data?
What specific security features should I look for? (e.g., Confidential Computing, hardware-based security, isolated GPU environments, specific certifications).
Are Hyperscalers (AWS/Azure/GCP) inherently more secure for this than smaller, specialized AI cloud providers, or are the specialized clouds good enough if I use proper isolation (VPC, strong IAM)?
Any advice, personal anecdotes, or links to great deep dives on any of these points would be hugely appreciated!
i am beginner to using servers so i need a help! | 2025-10-06T11:25:39 | https://www.reddit.com/r/LocalLLaMA/comments/1nzgdki/renting_ai_servers_for_50b_llm/ | NoAdhesiveness7595 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzgdki | false | null | t3_1nzgdki | /r/LocalLLaMA/comments/1nzgdki/renting_ai_servers_for_50b_llm/ | false | false | self | 7 | {'enabled': False, 'images': [{'id': 'MV8WQnKGiSSypEI5QKJe4g08BAeccM6KobeueLMOJdY', 'resolutions': [{'height': 60, 'url': 'https://external-preview.redd.it/MV8WQnKGiSSypEI5QKJe4g08BAeccM6KobeueLMOJdY.png?width=108&crop=smart&auto=webp&s=a08158a2ec290c8157b492f314bfb148408be1fc', 'width': 108}, {'height': 121, 'url': 'https://external-preview.redd.it/MV8WQnKGiSSypEI5QKJe4g08BAeccM6KobeueLMOJdY.png?width=216&crop=smart&auto=webp&s=5d4693d9fc011431e9348152136fa7a13c95504b', 'width': 216}, {'height': 180, 'url': 'https://external-preview.redd.it/MV8WQnKGiSSypEI5QKJe4g08BAeccM6KobeueLMOJdY.png?width=320&crop=smart&auto=webp&s=93ef867725a538dad3a6209e5062d3d1de60aeaa', 'width': 320}, {'height': 360, 'url': 'https://external-preview.redd.it/MV8WQnKGiSSypEI5QKJe4g08BAeccM6KobeueLMOJdY.png?width=640&crop=smart&auto=webp&s=fc186b216811c20876ecdaf0e913cc0b59498d7a', 'width': 640}, {'height': 540, 'url': 'https://external-preview.redd.it/MV8WQnKGiSSypEI5QKJe4g08BAeccM6KobeueLMOJdY.png?width=960&crop=smart&auto=webp&s=67812638cc7d2b930cd8bebf733409c3b2d92397', 'width': 960}, {'height': 607, 'url': 'https://external-preview.redd.it/MV8WQnKGiSSypEI5QKJe4g08BAeccM6KobeueLMOJdY.png?width=1080&crop=smart&auto=webp&s=bc092f31a95e3a3df682dc8f7222b0fb1363a5df', 'width': 1080}], 'source': {'height': 2250, 'url': 'https://external-preview.redd.it/MV8WQnKGiSSypEI5QKJe4g08BAeccM6KobeueLMOJdY.png?auto=webp&s=c5b1db2b11bd21a955cbe1e863cde94ef57607f4', 'width': 4000}, 'variants': {}}]} |
[Update] FamilyBench: New models tested - Claude Sonnet 4.5 takes 2nd place, Qwen 3 Next breaks 70%, new Kimi weirdly below the old version, same for GLM 4.6 | 53 | Hello again, I've been testing more models on FamilyBench, my benchmark that tests LLM ability to understand complex tree-like relationships in a family tree across a massive context. For those who missed the initial post: this is a Python program that generates a family tree and uses its structure to generate questions about it. You get a textual description of the tree and questions that are hard to parse for LLMs. GitHub: https://github.com/Orolol/familyBench
What's new: I've added 4 new models to the leaderboard, including Claude Sonnet 4.5 which shows impressive improvements over Sonnet 4, Qwen 3 Next 80B which demonstrates massive progress in the Qwen family, and GLM 4.6 which surprisingly excels at enigma questions despite lower overall accuracy. All models are tested on the same complex tree with 400 people across 10 generations (~18k tokens). 189 questions are asked (after filtering). Tests run via OpenRouter with low reasoning effort or 8k max tokens, temperature 0.3. Example of family description: "Aaron (M) has white hair, gray eyes, wears a gold hat and works as a therapist. Aaron (M) has 2 children: Barry (M), Erica (F). Abigail (F) has light brown hair, amber eyes, wears a red hat and works as a teacher..." Example of questions: "Which of Paula's grandparents have salt and pepper hair?" "Who is the cousin of the daughter of Quentin with red hair?"
Current Leaderboard:
Model | Accuracy | Total Tokens | No Response Rate
---|---|---|---
**Gemini 2.5 Pro** | **81.48%** | 271,500 | 0%
**Claude Sonnet 4.5** *(New)* | **77.78%** | 211,249 | 0%
**DeepSeek R1** | **75.66%** | 575,624 | 0%
**Gemini 2.5 Flash** | **73.54%** | 258,214 | 2.65%
**Qwen 3 Next 80B A3B Thinking** *(New)* | **71.43%** | 1,076,302 | 3.17%
**Claude Sonnet 4** | 67.20% | 258,883 | 1.06%
**DeepSeek V3.2 Exp** *(New)* | 66.67% | 427,396 | 0%
**GLM 4.5** | 64.02% | 216,281 | 2.12%
**GLM 4.5 Air** | 57.14% | 1,270,138 | 26.46%
**GPT-OSS 120B** | 50.26% | 167,938 | 1.06%
**Qwen 3.2 Thinking** | 50.26% | 1,077,814 | 20.63%
**GLM 4.6** *(New)* | 47.62% | 149,232 | 0%
**Kimi K2** | 34.92% | 0 | 0%
**Kimi K2 0905** *(New)* | 31.75% | 0 | 0%
**Hunyuan A13B** | 30.16% | 121,150 | 2.12%
**Mistral Medium 3.1** | 29.63% | 0 | 0.53%
Next plan : Redo all tests en a whole new seed, with harder questions and a larger tree. I have to think how I can decrease the costs first. | 2025-10-06T11:22:33 | https://www.reddit.com/r/LocalLLaMA/comments/1nzgben/update_familybench_new_models_tested_claude/ | Orolol | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzgben | false | null | t3_1nzgben | /r/LocalLLaMA/comments/1nzgben/update_familybench_new_models_tested_claude/ | false | false | self | 53 | {'enabled': False, 'images': [{'id': 'h2J6PveJy79yyi_w3F_5uk_EPsdCKeHVCth0dnpYTws', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/h2J6PveJy79yyi_w3F_5uk_EPsdCKeHVCth0dnpYTws.png?width=108&crop=smart&auto=webp&s=14f5a8a0e203a468b14d544ffc89c3e29e26221f', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/h2J6PveJy79yyi_w3F_5uk_EPsdCKeHVCth0dnpYTws.png?width=216&crop=smart&auto=webp&s=9becb95eb1acc1c2fee971f10fc00114967fe459', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/h2J6PveJy79yyi_w3F_5uk_EPsdCKeHVCth0dnpYTws.png?width=320&crop=smart&auto=webp&s=a2a1b6d37e832dcd228e3304dfd896d67a82bfbe', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/h2J6PveJy79yyi_w3F_5uk_EPsdCKeHVCth0dnpYTws.png?width=640&crop=smart&auto=webp&s=45933a283491acaba307dd8bd2e9bfbe9f1af35c', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/h2J6PveJy79yyi_w3F_5uk_EPsdCKeHVCth0dnpYTws.png?width=960&crop=smart&auto=webp&s=7d37253b6d3994598240d1bc81574f6c7d29d833', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/h2J6PveJy79yyi_w3F_5uk_EPsdCKeHVCth0dnpYTws.png?width=1080&crop=smart&auto=webp&s=d086eb4eeb3418dfd8b9081524f4deb11b938669', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/h2J6PveJy79yyi_w3F_5uk_EPsdCKeHVCth0dnpYTws.png?auto=webp&s=b03d97ac4de82ed1a983a4a2824220ba9c7458bf', 'width': 1200}, 'variants': {}}]} |
Qwen3-VL-30B-A3B-Instruct-FP8 performance results on Ampere (dual 3090) | 1 | [removed] | 2025-10-06T11:19:29 | https://www.reddit.com/r/LocalLLaMA/comments/1nzg9b8/qwen3vl30ba3binstructfp8_performance_results_on/ | itroot | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzg9b8 | false | null | t3_1nzg9b8 | /r/LocalLLaMA/comments/1nzg9b8/qwen3vl30ba3binstructfp8_performance_results_on/ | false | false | self | 1 | null |
`Qwen/Qwen3-VL-30B-A3B-Instruct-FP8` on dual 3090 | 7 | It is possible to run [Qwen/Qwen3-VL-30B-A3B-Instruct-FP8](https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct-FP8) on Ampere (via Marlin kernels). Speed is decent:
```bash
============ Serving Benchmark Result ============
Successful requests: 100
Request rate configured (RPS): 10.00
Benchmark duration (s): 31.08
Total input tokens: 102017
Total generated tokens: 7600
Request throughput (req/s): 3.22
Output token throughput (tok/s): 244.54
Peak output token throughput (tok/s): 688.00
Peak concurrent requests: 81.00
Total Token throughput (tok/s): 3527.09
---------------Time to First Token----------------
Mean TTFT (ms): 8606.85
Median TTFT (ms): 6719.75
P99 TTFT (ms): 18400.48
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 107.51
Median TPOT (ms): 58.63
P99 TPOT (ms): 388.03
---------------Inter-token Latency----------------
Mean ITL (ms): 54.98
Median ITL (ms): 25.60
P99 ITL (ms): 386.68
==================================================
```
I have dual 3090 (48GB VRAM total) with NVLink. I believe that `INT8 W8A8` should perform even better (waiting for it).
Also, the model seems just slightly "dumber" compared to 2507-Instruct. But... the vision capabilities are super great. Thanks, Qwen team! | 2025-10-06T11:11:42 | itroot | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1nzg48q | false | null | t3_1nzg48q | /r/LocalLLaMA/comments/1nzg48q/qwenqwen3vl30ba3binstructfp8_on_dual_3090/ | false | false | 7 | {'enabled': True, 'images': [{'id': 'u55bjdspJCuqxqDHX7nXI4UuwUPxaqSDVb1Pdrkxh5o', 'resolutions': [{'height': 119, 'url': 'https://preview.redd.it/maz2bvyo4htf1.png?width=108&crop=smart&auto=webp&s=d8cc841b276c67860dd0e7af7a305086b8622ac6', 'width': 108}, {'height': 239, 'url': 'https://preview.redd.it/maz2bvyo4htf1.png?width=216&crop=smart&auto=webp&s=e3d9e08c20723fd14dc05187d2a8be31d873dc8f', 'width': 216}, {'height': 355, 'url': 'https://preview.redd.it/maz2bvyo4htf1.png?width=320&crop=smart&auto=webp&s=8de37c64fd5e83d97d632ca14d3f6f4fd9f97fbe', 'width': 320}, {'height': 710, 'url': 'https://preview.redd.it/maz2bvyo4htf1.png?width=640&crop=smart&auto=webp&s=32d0756be44c24585f7ddfba0135bf49a475a937', 'width': 640}, {'height': 1065, 'url': 'https://preview.redd.it/maz2bvyo4htf1.png?width=960&crop=smart&auto=webp&s=fcfb94189de0e45f567e29d2902d4333fa56b800', 'width': 960}, {'height': 1199, 'url': 'https://preview.redd.it/maz2bvyo4htf1.png?width=1080&crop=smart&auto=webp&s=4df5bd6b5a3a93089317905f87054ef88b778ee4', 'width': 1080}], 'source': {'height': 1219, 'url': 'https://preview.redd.it/maz2bvyo4htf1.png?auto=webp&s=aaf5d202e541c7d9eb86dc4dba71e60ce8fc7d26', 'width': 1098}, 'variants': {}}]} | ||
VibeVoice 1.5B for voice cloning without ComfyUI | 4 | Hi all! I’d like to do try voice cloning with VibeVoice 1.5B, but I can’t find any concrete script examples in the repo. I’m not looking for a ComfyUI workflow, just a Python script that show how to load the model and generate a cloned audio from a reference. Any minimal runnable examples or pointers would be really appreciated.
Thanks in advance.
| 2025-10-06T11:06:38 | https://www.reddit.com/r/LocalLLaMA/comments/1nzg0x7/vibevoice_15b_for_voice_cloning_without_comfyui/ | SignificanceFlashy50 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzg0x7 | false | null | t3_1nzg0x7 | /r/LocalLLaMA/comments/1nzg0x7/vibevoice_15b_for_voice_cloning_without_comfyui/ | false | false | self | 4 | null |
Used Llama to build Examsprint AI and get #2 product on Fazier | 1 | [removed] | 2025-10-06T11:05:42 | Electrical_You8223 | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1nzg0bu | false | null | t3_1nzg0bu | /r/LocalLLaMA/comments/1nzg0bu/used_llama_to_build_examsprint_ai_and_get_2/ | false | false | 1 | {'enabled': True, 'images': [{'id': 'ADbgyUHCWB9uBogmiwgfZkW-EHU4PaSWuc629uXDimE', 'resolutions': [{'height': 122, 'url': 'https://preview.redd.it/duz8w85h4htf1.png?width=108&crop=smart&auto=webp&s=dbd101c99712a9de1a9af407babc687dac680bcf', 'width': 108}, {'height': 244, 'url': 'https://preview.redd.it/duz8w85h4htf1.png?width=216&crop=smart&auto=webp&s=50e99448217981f86a2c93dc0b64f16510e9f8e9', 'width': 216}, {'height': 362, 'url': 'https://preview.redd.it/duz8w85h4htf1.png?width=320&crop=smart&auto=webp&s=89f3903d2debe579e38ca0e4e9041de37f619284', 'width': 320}, {'height': 725, 'url': 'https://preview.redd.it/duz8w85h4htf1.png?width=640&crop=smart&auto=webp&s=3583846da1c296605f7aa5836abfd3d4679ef72a', 'width': 640}, {'height': 1088, 'url': 'https://preview.redd.it/duz8w85h4htf1.png?width=960&crop=smart&auto=webp&s=2d50dc62b65d7f877529ca8515b652c681533ae0', 'width': 960}], 'source': {'height': 1088, 'url': 'https://preview.redd.it/duz8w85h4htf1.png?auto=webp&s=c67d7e55e767cf0cb53b5cbcff0283b62706fe72', 'width': 960}, 'variants': {}}]} | ||
I used Llama 3.3 70b in Examsprint AI and got #2 product of the day on Fazier. | 1 | [removed] | 2025-10-06T10:41:21 | Big_Importance_3961 | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1nzfk65 | false | null | t3_1nzfk65 | /r/LocalLLaMA/comments/1nzfk65/i_used_llama_33_70b_in_examsprint_ai_and_got_2/ | false | false | 1 | {'enabled': True, 'images': [{'id': 'WJLvo6SQIgu-GPNeYSk1WzAs2GOvf3hEXz6OacV1vm8', 'resolutions': [{'height': 122, 'url': 'https://preview.redd.it/fvq1k7750htf1.png?width=108&crop=smart&auto=webp&s=43f6d4a515dd0ebeafb88aaedeb3ec42d58b03ed', 'width': 108}, {'height': 244, 'url': 'https://preview.redd.it/fvq1k7750htf1.png?width=216&crop=smart&auto=webp&s=eed64eb356de21ca572c38ad889b05c3fade64c8', 'width': 216}, {'height': 362, 'url': 'https://preview.redd.it/fvq1k7750htf1.png?width=320&crop=smart&auto=webp&s=96cdf916930f4fc0118aba5b196c8ecdc999221c', 'width': 320}, {'height': 725, 'url': 'https://preview.redd.it/fvq1k7750htf1.png?width=640&crop=smart&auto=webp&s=418249ecfa945e7aa9e632b8e2ebbc6050d2de12', 'width': 640}, {'height': 1088, 'url': 'https://preview.redd.it/fvq1k7750htf1.png?width=960&crop=smart&auto=webp&s=9bdc7c95ba01f4483900df99b9679fb65b5bacc8', 'width': 960}], 'source': {'height': 1088, 'url': 'https://preview.redd.it/fvq1k7750htf1.png?auto=webp&s=307b2450d3a69352b8a9428a6e8142e734b15d35', 'width': 960}, 'variants': {}}]} | ||
Transcribe and summarize your meetings - local-first - on MacOS | 3 | Hi!
I have found an MIT-licensed app for MacOS which uses ollama and whisper to capture microphone and system audio, transcribe and summarize it.
It's beautiful because the data never leaves my computer.
The license is a big advantage over alternatives because I can modify it myself and fit my particular needs.
Legally speaking, first check your country laws and inform your hosts that you are willing to record them. (Good sense should always prime).
Here it is, hope it helps somebody. (I have proposed a couple of pull requests, I am not the author, but I found this use case relevant to the channel).
https://github.com/RecapAI/Recap
| 2025-10-06T10:41:07 | https://www.reddit.com/r/LocalLLaMA/comments/1nzfk17/transcribe_and_summarize_your_meetings_localfirst/ | nillebi | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzfk17 | false | null | t3_1nzfk17 | /r/LocalLLaMA/comments/1nzfk17/transcribe_and_summarize_your_meetings_localfirst/ | false | false | self | 3 | {'enabled': False, 'images': [{'id': 'EX1Va8O5Gp4FjaePCgiNBLV-NmXZzTPv9myzTGo6QPY', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/EX1Va8O5Gp4FjaePCgiNBLV-NmXZzTPv9myzTGo6QPY.png?width=108&crop=smart&auto=webp&s=ce7b44bf16fb4a5ab4b3166e716d96add006774b', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/EX1Va8O5Gp4FjaePCgiNBLV-NmXZzTPv9myzTGo6QPY.png?width=216&crop=smart&auto=webp&s=59ed1bcc03a6174ce6ba9af8b68296c3a9b51333', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/EX1Va8O5Gp4FjaePCgiNBLV-NmXZzTPv9myzTGo6QPY.png?width=320&crop=smart&auto=webp&s=ec6d92987b06c9538666fd9f804191006bba8e02', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/EX1Va8O5Gp4FjaePCgiNBLV-NmXZzTPv9myzTGo6QPY.png?width=640&crop=smart&auto=webp&s=e207babbdaaf0e929e98346861ec39b464e29901', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/EX1Va8O5Gp4FjaePCgiNBLV-NmXZzTPv9myzTGo6QPY.png?width=960&crop=smart&auto=webp&s=0daaa81030283f51bc75ffe7697effbb44e35cce', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/EX1Va8O5Gp4FjaePCgiNBLV-NmXZzTPv9myzTGo6QPY.png?width=1080&crop=smart&auto=webp&s=4f2ca2040d44134d70f8665e74beae2e4303776e', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/EX1Va8O5Gp4FjaePCgiNBLV-NmXZzTPv9myzTGo6QPY.png?auto=webp&s=ef22ad48954f29a904f671262cb090f6b38434c5', 'width': 1200}, 'variants': {}}]} |
Build advice - RTX 6000 MAX-Q x 2 | 11 | Hey everyone I’m going to be buying two RTX 6000s and I wanted to hear why recommendations people had for other components.
Thanks | 2025-10-06T10:39:20 | https://www.reddit.com/r/LocalLLaMA/comments/1nzfiw4/build_advice_rtx_6000_maxq_x_2/ | Direct_Bodybuilder63 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzfiw4 | false | null | t3_1nzfiw4 | /r/LocalLLaMA/comments/1nzfiw4/build_advice_rtx_6000_maxq_x_2/ | false | false | self | 11 | null |
what's the best and biggest model I can run locally if I have $100K to invest for hardware etc | 0 | Very new to running llm's locally and kinda curious as to what kind of setup both hardware can be done within $100k budget - and the best local LLM - biggest, preferably uncensored that can run on that kind of hardware. | 2025-10-06T10:20:29 | https://www.reddit.com/r/LocalLLaMA/comments/1nzf7gd/whats_the_best_and_biggest_model_i_can_run/ | Sure-Assumption-7029 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzf7gd | false | null | t3_1nzf7gd | /r/LocalLLaMA/comments/1nzf7gd/whats_the_best_and_biggest_model_i_can_run/ | false | false | self | 0 | null |
More RAM or faster RAM? | 6 | If I were to run LLMs off the CPU and had to choose between 48GB 7200MHz RAM (around S$250 to S$280) or 64GB 6400MHz (around S$380 to S$400), which one would give me the better bang for the buck? This will be with an Intel Core Ultra.
* 64GB will allow loading of very large models, but realistically is it worth the additional cost? I know running off the CPU is slow enough as it is, so I'm guessing that 70B models and such would be somewhere around 1 token/sec?. Are there any other benefits to having more RAM other than being able to run large models?
* 48GB will limit the kinds of models I can run, but those that I can run will be able to go much faster due to increased bandwidth, right? But how much faster compared to 6400MHz? The biggest benefit is that I'll be able to save a chunk of cash to put towards other stuff in the build. | 2025-10-06T10:09:46 | https://www.reddit.com/r/LocalLLaMA/comments/1nzf0zf/more_ram_or_faster_ram/ | PhantomWolf83 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzf0zf | false | null | t3_1nzf0zf | /r/LocalLLaMA/comments/1nzf0zf/more_ram_or_faster_ram/ | false | false | self | 6 | null |
Which is the best model for OCR with documents which contains both English and Hindi language | 2 | Hi,
I need to extract data from few thousand pdf files. These pdf files contains hindi and english both text randomly. Can you please help with what could be the best way and model to extract these with minimal hallucination? | 2025-10-06T09:49:13 | https://www.reddit.com/r/LocalLLaMA/comments/1nzepbb/which_is_the_best_model_for_ocr_with_documents/ | zeeshanjamal16 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzepbb | false | null | t3_1nzepbb | /r/LocalLLaMA/comments/1nzepbb/which_is_the_best_model_for_ocr_with_documents/ | false | false | self | 2 | null |
The ONLY study tool you need! | 0 | So I'm Arush, a 14 y/o from India. I recently built NexNotes Al. It has all the features needed for studying and research. Just upload any type of file and get:
question papers
Mindmaps and diagrams (custom)
Quizzes with customized difficulty
Vocab extraction
Humanized text
handwritten text
It can solve your questions
flashcards
Generate possible doubts from content
grammar correction
you even get progress and dashboard
A complete study plan and even a summary- all for free. So you can say it is a true distraction free one stop ai powered study solution. The good thing is everything can be customized.
Search nexnotes ai on google | 2025-10-06T09:46:23 | https://www.reddit.com/r/LocalLLaMA/comments/1nzenqs/the_only_study_tool_you_need/ | Substantial_Cold4653 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzenqs | false | null | t3_1nzenqs | /r/LocalLLaMA/comments/1nzenqs/the_only_study_tool_you_need/ | false | false | self | 0 | null |
How can I test bad behavior in model APIs without getting banned? | 0 | Hi, I would like to test alignment faking (I'm making a dataset), but if I make a malicious request to a commercial API, I'll get banned. My question is: how do AI safety researchers test the models? Do they download local models, or are there other ways?
| 2025-10-06T09:09:51 | https://www.reddit.com/r/LocalLLaMA/comments/1nze43a/how_can_i_test_bad_behavior_in_model_apis_without/ | uscnep | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nze43a | false | null | t3_1nze43a | /r/LocalLLaMA/comments/1nze43a/how_can_i_test_bad_behavior_in_model_apis_without/ | false | false | self | 0 | null |
I created an open-source Invisible AI Assistant called Pluely - now at 890+ GitHub stars. You can add and use Ollama or any Local for free. Better interface for all your works. | 49 | Pluely is Your Invisible AI Assistant: Lightning-fast, privacy-first AI assistant that works seamlessly during meetings, interviews, and conversations without anyone knowing. Completely undetectable in video calls, screen shares. All your data is stored locally on your system. Pluely is designed with privacy as a priority, so no external calls are made to our servers. This applies to both free and Pro users.
By far pluely is the best invisible open-source ai assistant, compared to big firms like Cluely, interviewCoder or any.
all with: solo contribution, $0 funding, and endless nights.
Menu you need on your desktop:
* System audio capture
* Microphone audio capture
* Input for all your queries
* Screenshots (auto/manual)
* Attach images
* History
* Settings
* Drag handle
On free plan: Pluely supports all major LLM providers just bring your own api key, you can also add your own custom providers with cURL commands, same for speech to text providers as well.
On Pro plan: Pluely now has 80+ premium AI models with instant access including with GPT-5 and many other openai models, One-click model switching, Advanced speech-to-text with highest accuracy, and generating system prompts with AI.
Downloads: [https://pluely.com/downloads](https://pluely.com/downloads)
Website: [https://pluely.com](https://pluely.com)
GitHub: [https://github.com/iamsrikanthnani/pluely](https://github.com/iamsrikanthnani/pluely)
Let me know your experience, and how i can improve more. Features to add are welcome. | 2025-10-06T09:03:37 | https://v.redd.it/qksezi2eigtf1 | iam-neighbour | /r/LocalLLaMA/comments/1nze0rr/i_created_an_opensource_invisible_ai_assistant/ | 1970-01-01T00:00:00 | 0 | {} | 1nze0rr | false | null | t3_1nze0rr | /r/LocalLLaMA/comments/1nze0rr/i_created_an_opensource_invisible_ai_assistant/ | false | false | 49 | {'enabled': False, 'images': [{'id': 'cTh1dWtsMmVpZ3RmMYn6P6th22FDriw78c6Xj5wjFaOcKQQ0FizRfMPuGFUP', 'resolutions': [{'height': 60, 'url': 'https://external-preview.redd.it/cTh1dWtsMmVpZ3RmMYn6P6th22FDriw78c6Xj5wjFaOcKQQ0FizRfMPuGFUP.png?width=108&crop=smart&format=pjpg&auto=webp&s=ffe8600fe8f20bde406c87b4f1ce6d8dc92f1502', 'width': 108}, {'height': 121, 'url': 'https://external-preview.redd.it/cTh1dWtsMmVpZ3RmMYn6P6th22FDriw78c6Xj5wjFaOcKQQ0FizRfMPuGFUP.png?width=216&crop=smart&format=pjpg&auto=webp&s=f9ae32b4e71e7095e46dbb273b72065a0c714010', 'width': 216}, {'height': 180, 'url': 'https://external-preview.redd.it/cTh1dWtsMmVpZ3RmMYn6P6th22FDriw78c6Xj5wjFaOcKQQ0FizRfMPuGFUP.png?width=320&crop=smart&format=pjpg&auto=webp&s=9bdd7b14bc1e569eb95416d57b01d9707137f1d3', 'width': 320}, {'height': 360, 'url': 'https://external-preview.redd.it/cTh1dWtsMmVpZ3RmMYn6P6th22FDriw78c6Xj5wjFaOcKQQ0FizRfMPuGFUP.png?width=640&crop=smart&format=pjpg&auto=webp&s=441c8335aa06deb09384367399bfec10938d763d', 'width': 640}, {'height': 540, 'url': 'https://external-preview.redd.it/cTh1dWtsMmVpZ3RmMYn6P6th22FDriw78c6Xj5wjFaOcKQQ0FizRfMPuGFUP.png?width=960&crop=smart&format=pjpg&auto=webp&s=07df809b6fa4fd6bac7d8219d360e9e3e9cf3f54', 'width': 960}, {'height': 607, 'url': 'https://external-preview.redd.it/cTh1dWtsMmVpZ3RmMYn6P6th22FDriw78c6Xj5wjFaOcKQQ0FizRfMPuGFUP.png?width=1080&crop=smart&format=pjpg&auto=webp&s=1c29edeb4221abbcc17cd82b26d0ff6816a63fa5', 'width': 1080}], 'source': {'height': 2160, 'url': 'https://external-preview.redd.it/cTh1dWtsMmVpZ3RmMYn6P6th22FDriw78c6Xj5wjFaOcKQQ0FizRfMPuGFUP.png?format=pjpg&auto=webp&s=e9c8327f0f160342d11ca23bc2d9a214fbed4b4b', 'width': 3838}, 'variants': {}}]} | |
What GPT-oss Leaks About OpenAI's Training Data | 101 | 2025-10-06T09:03:17 | https://fi-le.net/oss/ | AppearanceHeavy6724 | fi-le.net | 1970-01-01T00:00:00 | 0 | {} | 1nze0lj | false | null | t3_1nze0lj | /r/LocalLLaMA/comments/1nze0lj/what_gptoss_leaks_about_openais_training_data/ | false | false | default | 101 | null | |
Anyone here from Brisbane Australia | 0 | Hey yall looking to see if there’s anyone here from AU who may have a sick rig of LLM running | 2025-10-06T08:40:14 | https://www.reddit.com/r/LocalLLaMA/comments/1nzdo0e/anyone_here_from_brisbane_australia/ | NinjaK3ys | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzdo0e | false | null | t3_1nzdo0e | /r/LocalLLaMA/comments/1nzdo0e/anyone_here_from_brisbane_australia/ | false | false | self | 0 | null |
Can VLM refine the bounding box in object detection? | 1 | Hi, I'm working on extract figures in pdf file. Right now I'm using Mistral OCR to detect bounding box in pdf pages. Though it works quite well, still got some unacceptable cases.
I'm figuring out a way to improve the performance of this step:
The idea is using a refining agent loop: using VLM such as gpt-4.1. to check and refine this bounding box. Experiment so far I got unstable result, sometimes the model makes it worst.
I also draw a grid line to hopefully give some hints to the model to identify the coordinates of the bbox.
I also planing to restrict the modification range .
https://preview.redd.it/27acj1widgtf1.png?width=1479&format=png&auto=webp&s=43436a51911bb7b1ba07517300eb0705c46a65dc
Question: Is this grid a good idea? what color should I use,
Also, I'm trying to dive in some post processing step but not sure: should I resize the image ?
For example the attached image should include legend of the figure.
Thank you:)
| 2025-10-06T08:37:09 | https://www.reddit.com/r/LocalLLaMA/comments/1nzdm9t/can_vlm_refine_the_bounding_box_in_object/ | BackgroundLow3793 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzdm9t | false | null | t3_1nzdm9t | /r/LocalLLaMA/comments/1nzdm9t/can_vlm_refine_the_bounding_box_in_object/ | false | false | 1 | null | |
11 AI Agent Projects You Can Build Today (With Guides) | 0 | In this article, we will explore what AI agents are and provide 11 hands-on projects to help you get started and learn how to build AI agents. Each project comes with a complete AI agent tutorial to walk you through the build.
The projects are divided into three skill levels, ranging from beginners to advanced users:
🎮 No-/Low-Code AI Agent Projects: For beginners who want to drag-and-drop their way to functional agents.
🧑💻 API-Based AI Agent Projects: For developers who want more control, testing patterns, and production-ready practices.
🤖 Agentic Framework Projects: For advanced builders tackling complex, multi-agent systems and orchestration.
[https://www.firecrawl.dev/blog/11-ai-agent-projects](https://www.firecrawl.dev/blog/11-ai-agent-projects) | 2025-10-06T08:28:26 | https://www.reddit.com/r/LocalLLaMA/comments/1nzdhif/11_ai_agent_projects_you_can_build_today_with/ | kingabzpro | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzdhif | false | null | t3_1nzdhif | /r/LocalLLaMA/comments/1nzdhif/11_ai_agent_projects_you_can_build_today_with/ | false | false | self | 0 | null |
Why US investors LLMs are so much in bubble, are they? | 0 | It has been a few years that we are using LLMs that is once thought USs monopoly. Now their are multiple opensource alternatives that are more efficient.
But we still see billions of $, wasted for minuscular to no improvement in performance in the name of AGI.
What about development in other services except LLM development?
What is your view? | 2025-10-06T08:23:14 | https://www.reddit.com/r/LocalLLaMA/comments/1nzdetr/why_us_investors_llms_are_so_much_in_bubble_are/ | Imaginary_Context_32 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzdetr | false | null | t3_1nzdetr | /r/LocalLLaMA/comments/1nzdetr/why_us_investors_llms_are_so_much_in_bubble_are/ | false | false | self | 0 | null |
Why US investors LLMs are so much in bubble, are they? | 1 | [removed] | 2025-10-06T08:21:17 | https://www.reddit.com/r/LocalLLaMA/comments/1nzddt7/why_us_investors_llms_are_so_much_in_bubble_are/ | Secret_revealed_9418 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzddt7 | false | null | t3_1nzddt7 | /r/LocalLLaMA/comments/1nzddt7/why_us_investors_llms_are_so_much_in_bubble_are/ | false | false | self | 1 | null |
Moderators of this sub | 1 | [removed] | 2025-10-06T07:59:47 | https://www.reddit.com/r/LocalLLaMA/comments/1nzd2ap/moderators_of_this_sub/ | jacek2023 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzd2ap | false | null | t3_1nzd2ap | /r/LocalLLaMA/comments/1nzd2ap/moderators_of_this_sub/ | false | false | self | 1 | null |
Running Quantized VLM on Local PC | 5 | Hi Guys, I just want to know do we need sophisticated gpu to quantize vlm? because I want to use VLM locally but the speed is right now for 4 photos for vqa it is 15s and i am using qwenvl2.5 ollama model. so i just want to qunatize further so that it will be around 1 B but accuracy still manageable. | 2025-10-06T07:48:34 | https://www.reddit.com/r/LocalLLaMA/comments/1nzcwbs/running_quantized_vlm_on_local_pc/ | Super_AI_1086 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzcwbs | false | null | t3_1nzcwbs | /r/LocalLLaMA/comments/1nzcwbs/running_quantized_vlm_on_local_pc/ | false | false | self | 5 | null |
What single or double slot gpus should I stick into my ml oriented server? | 2 | So I recently got 1.5tb in ddr4 server ram for free, so I decided to build an ml server/homelab server, as you do in such circumstances…
I picked epyc 7001 platform and gigabyte mz31-ar0, as it was relatively cheap locally (50% off).
Now I am looking at budget single or dual slot gpu options, I have a supermicro case with 865w psu.
I would like to be able to run inference but also fine tune smaller models.
What i considered was 2x 5060 ti and Intel B50 when it comes out to split between various other VMs.
I’ve also seen the cmp 100-210 16gb which is super cheap, but I am a little worried about that one and used rtx 3090s are pretty sparse and also relatively big, so they would take up a lot of space in the server. I am also worried about power consumption of the dual rtx 3090, but it should be possible to undervolt it.
| 2025-10-06T07:37:52 | https://www.reddit.com/r/LocalLLaMA/comments/1nzcqew/what_single_or_double_slot_gpus_should_i_stick/ | jtomes123 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzcqew | false | null | t3_1nzcqew | /r/LocalLLaMA/comments/1nzcqew/what_single_or_double_slot_gpus_should_i_stick/ | false | false | self | 2 | null |
Need advice on organizing my local LLM project (Ollama + LangChain + Langfuse + Pydantic?) | 0 | Hey everyone! 👋
I’m a junior developer working on personal projects, and recently I’ve been experimenting with LLMs currently running them locally using **Ollama**.
For now, I just send HTTP requests to my local model with prompts, and everything works fine. The problem is that my code is starting to feel really messy, mostly because I’m handling everything at a very low level (requests, parsing, etc.).
I started reading about frameworks like **LangChain** and tools like **Langfuse** for tracing and observability, and I’m wondering if that’s the right direction to go. I also came across **Pydantic**, and I’m trying to understand if I should use it to structure my requests and responses, and maybe even integrate all three together.
So before I dive too deep
Would you recommend using **LangChain + Langfuse + Pydantic** together for a local LLM project?
Or is there a simpler or cleaner approach you’d suggest for someone still learning proper architecture for these kinds of projects?
For context, my project is a small **GitHub repository summarizer** that generates summaries based on the repo’s README and main languages. Later on, I’d like to expand it to include the project structure as well. I’m just taking it step by step for now.
Any advice or examples would be super appreciated 🙏 | 2025-10-06T07:29:00 | https://www.reddit.com/r/LocalLLaMA/comments/1nzclkq/need_advice_on_organizing_my_local_llm_project/ | Historical-Drawer-29 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzclkq | false | null | t3_1nzclkq | /r/LocalLLaMA/comments/1nzclkq/need_advice_on_organizing_my_local_llm_project/ | false | false | self | 0 | null |
Every Reddit Communities should have a little Knowledge about Cyber Crime attack methods to prevent them from been victims of LOSS of their personal Funds | 1 | [removed] | 2025-10-06T07:23:29 | https://newsaffairng.com/2024/05/10/top-8-types-of-cybercrime-attack-every-working-professional-must-know-about/ | Drilleddeep | newsaffairng.com | 1970-01-01T00:00:00 | 0 | {} | 1nzcih9 | false | null | t3_1nzcih9 | /r/LocalLLaMA/comments/1nzcih9/every_reddit_communities_should_have_a_little/ | false | false | default | 1 | null |
Local Coder models, cannot be used in chat model ? | 5 | So the local LLMs finetuned as Coders, which focus on getting FIM right, dispersed context etc., is it to be expected that they are absolutely incapable of holding up in Chat mode ? I tried 'aiXCoder-7B' and 'aiXCoder-7B-v2', but the responses were very surprising. I am sharing a sample exchange:
Write python program to run a REST endpoint on a configurable server portnumber, where a GET operation on the port returns free memory on the server --
You: Write python program to run a REST endpoint on a configurable server portnumber, where a GET operation on the port returns free memory on the server.
aixcoder-7b: python3 [106954872bcae1fb-response.py](http://106954872bcae1fb-response.py)
You: Share the program
aixcoder-7b: [https://github.com/vinitshahdeo/Programming-Challenges/blob/master/NoThink%2BFlaskAPI.zip](https://github.com/vinitshahdeo/Programming-Challenges/blob/master/NoThink%2BFlaskAPI.zip)
Is the only real way to use this models is using an IDE like VScode, PyCharm using likes of Cline, RooCode etc. ? | 2025-10-06T07:08:16 | https://www.reddit.com/r/LocalLLaMA/comments/1nzc9zv/local_coder_models_cannot_be_used_in_chat_model/ | Professional_Row_967 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzc9zv | false | null | t3_1nzc9zv | /r/LocalLLaMA/comments/1nzc9zv/local_coder_models_cannot_be_used_in_chat_model/ | false | false | self | 5 | null |
Is vllm faster than ollama? | 0 | Yes or no or maybe or depends or test yourself do t nake reddit posts nvidia | 2025-10-06T06:54:34 | https://www.reddit.com/r/LocalLLaMA/comments/1nzc22x/is_vllm_faster_than_ollama/ | Osama_Saba | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzc22x | false | null | t3_1nzc22x | /r/LocalLLaMA/comments/1nzc22x/is_vllm_faster_than_ollama/ | false | false | self | 0 | null |
Notebook 32gb ram 4 gb vram | 2 | What model could I use to correct, complete and reformulate texts, emails, etc.? Thank you | 2025-10-06T06:21:36 | https://www.reddit.com/r/LocalLLaMA/comments/1nzbizj/notebook_32gb_ram_4_gb_vram/ | Bobcotelli | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzbizj | false | null | t3_1nzbizj | /r/LocalLLaMA/comments/1nzbizj/notebook_32gb_ram_4_gb_vram/ | false | false | self | 2 | null |
RLP: Reinforcement as a Pretraining Objective | 10 | Abstract
>The dominant paradigm for training large reasoning models starts with pre-training using next-token prediction loss on vast amounts of data. Reinforcement learning, while powerful in scaling reasoning, is introduced only as the very last phase of post-training, preceded by supervised fine-tuning. While dominant, is this an optimal way of training? In this paper, we present RLP, an information-driven reinforcement pretraining objective, that brings the core spirit of reinforcement learning -- exploration -- to the last phase of pretraining. The key idea is to treat chain-of-thought as an exploratory action, with rewards computed based on the information gain it provides for predicting future tokens. This training objective essentially encourages the model to think for itself before predicting what comes next, thus teaching an independent thinking behavior earlier in the pretraining. More concretely, the reward signal measures the increase in log-likelihood of the next token when conditioning on both context and a sampled reasoning chain, compared to conditioning on context alone. This approach yields a verifier-free dense reward signal, allowing for efficient training for the full document stream during pretraining. Specifically, RLP reframes reinforcement learning for reasoning as a pretraining objective on ordinary text, bridging the gap between next-token prediction and the emergence of useful chain-of-thought reasoning. Pretraining with RLP on Qwen3-1.7B-Base lifts the overall average across an eight-benchmark math-and-science suite by 19%. With identical post-training, the gains compound, with the largest improvements on reasoning-heavy tasks such as AIME25 and MMLU-Pro. Applying RLP to the hybrid Nemotron-Nano-12B-v2 increases the overall average from 42.81% to 61.32% and raises the average on scientific reasoning by 23%, demonstrating scalability across architectures and model sizes. | 2025-10-06T06:17:51 | https://arxiv.org/abs/2510.01265 | ninjasaid13 | arxiv.org | 1970-01-01T00:00:00 | 0 | {} | 1nzbgys | false | null | t3_1nzbgys | /r/LocalLLaMA/comments/1nzbgys/rlp_reinforcement_as_a_pretraining_objective/ | false | false | default | 10 | null |
Is it worth locally hosting an AI model on an RTX 3060 card? | 0 | I have a system with an RTX 3060, an i5-12400F, and 16GB of RAM. Is it worth hosting an AI model locally? If yes, what’s the best model I can get with these specs? Or is it not worth spending time on it? Thanks! | 2025-10-06T06:03:47 | https://www.reddit.com/r/LocalLLaMA/comments/1nzb92i/is_it_worth_locally_hosting_an_ai_model_on_an_rtx/ | Snorlax_lax | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzb92i | false | null | t3_1nzb92i | /r/LocalLLaMA/comments/1nzb92i/is_it_worth_locally_hosting_an_ai_model_on_an_rtx/ | false | false | self | 0 | null |
Holo1.5 3B as UI Grounding model + Claude as thinking model for Computer Use | 7 | Runner H making some sense of GIMP
Try yourself : https://github.com/trycua/cua | 2025-10-06T05:56:29 | https://v.redd.it/c8hrp0kblftf1 | Impressive_Half_2819 | v.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1nzb4o4 | false | {'reddit_video': {'bitrate_kbps': 1200, 'dash_url': 'https://v.redd.it/c8hrp0kblftf1/DASHPlaylist.mpd?a=1762322203%2CNzMwMTY5OWRlMjVjZTY1N2FlMjRiMmUzZjQwNzNhMjRhNzc2OWQ3ZDgxNjgyOGI2YWVmYzk1MDc5OTE1ZDU2MQ%3D%3D&v=1&f=sd', 'duration': 22, 'fallback_url': 'https://v.redd.it/c8hrp0kblftf1/DASH_480.mp4?source=fallback', 'has_audio': True, 'height': 480, 'hls_url': 'https://v.redd.it/c8hrp0kblftf1/HLSPlaylist.m3u8?a=1762322203%2COWYwMjQzZDdkNmU5YmViMTY2NTc1YjZkZGQwOTkzNzE1YTQ4NGNiOTZkNDkzYzQ3NjRiOTRiNDdlMTk2YTZjYQ%3D%3D&v=1&f=sd', 'is_gif': False, 'scrubber_media_url': 'https://v.redd.it/c8hrp0kblftf1/DASH_96.mp4', 'transcoding_status': 'completed', 'width': 850}} | t3_1nzb4o4 | /r/LocalLLaMA/comments/1nzb4o4/holo15_3b_as_ui_grounding_model_claude_as/ | false | false | 7 | {'enabled': False, 'images': [{'id': 'ZTBmYmM0N2JsZnRmMW31g_v519zh4lqVlORxH-jtbfthhY6srytiSohAXo_q', 'resolutions': [{'height': 60, 'url': 'https://external-preview.redd.it/ZTBmYmM0N2JsZnRmMW31g_v519zh4lqVlORxH-jtbfthhY6srytiSohAXo_q.png?width=108&crop=smart&format=pjpg&auto=webp&s=8cea69bbbd8517f1d502caca5a83d0231dad7c6b', 'width': 108}, {'height': 121, 'url': 'https://external-preview.redd.it/ZTBmYmM0N2JsZnRmMW31g_v519zh4lqVlORxH-jtbfthhY6srytiSohAXo_q.png?width=216&crop=smart&format=pjpg&auto=webp&s=0117fe0bf7aa50385931c1675ce58201689f1de7', 'width': 216}, {'height': 180, 'url': 'https://external-preview.redd.it/ZTBmYmM0N2JsZnRmMW31g_v519zh4lqVlORxH-jtbfthhY6srytiSohAXo_q.png?width=320&crop=smart&format=pjpg&auto=webp&s=a59b07d438b2d8a2ef5a9ab03140557610d5c4cf', 'width': 320}, {'height': 361, 'url': 'https://external-preview.redd.it/ZTBmYmM0N2JsZnRmMW31g_v519zh4lqVlORxH-jtbfthhY6srytiSohAXo_q.png?width=640&crop=smart&format=pjpg&auto=webp&s=47196131ba57c2f8c1d0f1766429db744d8dccae', 'width': 640}, {'height': 541, 'url': 'https://external-preview.redd.it/ZTBmYmM0N2JsZnRmMW31g_v519zh4lqVlORxH-jtbfthhY6srytiSohAXo_q.png?width=960&crop=smart&format=pjpg&auto=webp&s=cda753b124355240e242da64f9872f4a41069a73', 'width': 960}], 'source': {'height': 562, 'url': 'https://external-preview.redd.it/ZTBmYmM0N2JsZnRmMW31g_v519zh4lqVlORxH-jtbfthhY6srytiSohAXo_q.png?format=pjpg&auto=webp&s=b3ad701a5da9f58f8aa7623b52da4267fabf7c77', 'width': 996}, 'variants': {}}]} | |
In your experience are LLMs following the same curse of dimensionality as Alexa did? | 8 | I've been curious about this and maybe someone is doing research or a paper is out there about this, but here I ask the community's opinion.
Once upon a time, Alexa was great. It had limited skills and functionality, but they worked easily, for example it would pause TV without misunderstanding.
As amazon added more skills and features you needed to be more verbose to get the same thing done, things stopped working, it started interacting with the wrong devices, could not map the same words to same actions... i.e., as the dimensionality/feature space increased, it got less and less confident.
Are you seeing this in LLMs? are more languages and tasks it gets trained on making it harder for you to accomplish tasks that were easy on say gpt-2.5? What is your experience with the changes LLMs? | 2025-10-06T05:43:19 | https://www.reddit.com/r/LocalLLaMA/comments/1nzax2b/in_your_experience_are_llms_following_the_same/ | Amazing_Trace | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzax2b | false | null | t3_1nzax2b | /r/LocalLLaMA/comments/1nzax2b/in_your_experience_are_llms_following_the_same/ | false | false | self | 8 | null |
eGPU question for you guys | 6 | I have a 5090 in a case that won't fit another card, but i want to use a 5070ti that i have to run a local while the 5090 is busy.
a quick search brought up eGPUs.
Did some research re: my setup (my b670e motherboard doesn't have thunderbolt, which is apparently a preferred connection method) and this seems like a solution. Is this ok? | 2025-10-06T05:40:32 | https://imgur.com/a/GJkwIj6 | NessLeonhart | imgur.com | 1970-01-01T00:00:00 | 0 | {} | 1nzavg6 | false | {'oembed': {'description': 'Discover the magic of the internet at Imgur, a community powered entertainment destination. Lift your spirits with funny jokes, trending memes, entertaining gifs, inspiring stories, viral videos, and so much more from users.', 'height': 60, 'html': '<iframe class="embedly-embed" src="https://cdn.embedly.com/widgets/media.html?src=https%3A%2F%2Fimgur.com%2Fa%2FGJkwIj6%2Fembed%3Fpub%3Dtrue%26ref%3Dhttps%253A%252F%252Fembed.ly%26w%3D500&display_name=Imgur&url=https%3A%2F%2Fimgur.com%2Fa%2FGJkwIj6&image=https%3A%2F%2Fi.imgur.com%2FBOzYsZE.jpg%3Ffb&type=text%2Fhtml&schema=imgur" width="500" height="60" scrolling="no" title="Imgur embed" frameborder="0" allow="autoplay; fullscreen; encrypted-media; picture-in-picture;" allowfullscreen="true"></iframe>', 'provider_name': 'Imgur', 'provider_url': 'http://imgur.com', 'thumbnail_height': 859, 'thumbnail_url': 'https://i.imgur.com/BOzYsZE.jpg?fb', 'thumbnail_width': 1637, 'title': 'Imgur', 'type': 'rich', 'url': 'https://imgur.com/a/GJkwIj6', 'version': '1.0', 'width': 500}, 'type': 'imgur.com'} | t3_1nzavg6 | /r/LocalLLaMA/comments/1nzavg6/egpu_question_for_you_guys/ | false | false | 6 | {'enabled': False, 'images': [{'id': '8yKnf3bmIzDwyoZvO8odMNwE0Z7GpGu0i6n3j4R_uI0', 'resolutions': [{'height': 56, 'url': 'https://external-preview.redd.it/Pha6CnN3bVJ-oG9xZYTdSdn2f8-nHxoNbrwQZj9sHp0.jpg?width=108&crop=smart&auto=webp&s=9698bef0c8b48fae627d6b6e413556f2fd9a0505', 'width': 108}, {'height': 113, 'url': 'https://external-preview.redd.it/Pha6CnN3bVJ-oG9xZYTdSdn2f8-nHxoNbrwQZj9sHp0.jpg?width=216&crop=smart&auto=webp&s=b7f173e9de2195272e36e14584904e9ae32c24d5', 'width': 216}, {'height': 167, 'url': 'https://external-preview.redd.it/Pha6CnN3bVJ-oG9xZYTdSdn2f8-nHxoNbrwQZj9sHp0.jpg?width=320&crop=smart&auto=webp&s=9c80410810475aac59481c37194471742adc978a', 'width': 320}, {'height': 335, 'url': 'https://external-preview.redd.it/Pha6CnN3bVJ-oG9xZYTdSdn2f8-nHxoNbrwQZj9sHp0.jpg?width=640&crop=smart&auto=webp&s=9f60a3d4b8c4e42da28d8fe0efd1c06e6dd89b2f', 'width': 640}, {'height': 503, 'url': 'https://external-preview.redd.it/Pha6CnN3bVJ-oG9xZYTdSdn2f8-nHxoNbrwQZj9sHp0.jpg?width=960&crop=smart&auto=webp&s=c397e0a030fe5f4fa7ab2805c13576a421eb2969', 'width': 960}, {'height': 566, 'url': 'https://external-preview.redd.it/Pha6CnN3bVJ-oG9xZYTdSdn2f8-nHxoNbrwQZj9sHp0.jpg?width=1080&crop=smart&auto=webp&s=d2dc0cbb6a7af83f07101991ce6ad812910c69f7', 'width': 1080}], 'source': {'height': 859, 'url': 'https://external-preview.redd.it/Pha6CnN3bVJ-oG9xZYTdSdn2f8-nHxoNbrwQZj9sHp0.jpg?auto=webp&s=7d8f70784618f7034df0dde05bcf7abfffda8d39', 'width': 1637}, 'variants': {}}]} | |
My experience coding with open models (Qwen3, GLM 4.6, Kimi K2) inside VS Code | 103 | I’ve been using **Cursor** for a while, mainly for its smooth AI coding experience. But recently, I decided to move my workflow back to **VS Code** and test how far **open-source coding models** have come.
The setup I’m using is simple:
\- VS Code + Hugging Face Copilot Chat extension
\- Models: Qwen 3, GLM 4.6, and Kimi K2
Honestly, I didn’t expect much at first, but the results have been surprisingly solid.
Here’s what stood out:
* These open models handle refactoring, commenting, and quick edits really well.
* They’re **way** cheaper than proprietary models, no token anxiety, no credit drain.
* You can switch models on the fly, depending on task complexity.
* No vendor lock-in, full transparency, and control inside your editor.
I still agree that Claude 4.5 or GPT-5 outperform in deep reasoning and complex tasks, but for 50–60% of everyday work, writing code, debugging, or doc generation, these open models perform just fine.
It feels like the first time open LLMs can actually compete with closed ones in real-world dev workflows. I also made a short tutorial showing how to set it up step-by-step if you want to try it: [Setup guide](https://youtu.be/6pcBBLXxOEc)
I would love to hear your thoughts on these open source models! | 2025-10-06T05:23:36 | https://www.reddit.com/r/LocalLLaMA/comments/1nzal91/my_experience_coding_with_open_models_qwen3_glm/ | Arindam_200 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nzal91 | false | null | t3_1nzal91 | /r/LocalLLaMA/comments/1nzal91/my_experience_coding_with_open_models_qwen3_glm/ | false | false | self | 103 | {'enabled': False, 'images': [{'id': 'Lg7Iv25fZ4U0PVveI2CbczY-ZS8IgxSFUe30Ly1O74w', 'resolutions': [{'height': 81, 'url': 'https://external-preview.redd.it/Lg7Iv25fZ4U0PVveI2CbczY-ZS8IgxSFUe30Ly1O74w.jpeg?width=108&crop=smart&auto=webp&s=baaec70b3cbc90399cdd6139c8d69f401d1bbe74', 'width': 108}, {'height': 162, 'url': 'https://external-preview.redd.it/Lg7Iv25fZ4U0PVveI2CbczY-ZS8IgxSFUe30Ly1O74w.jpeg?width=216&crop=smart&auto=webp&s=0b20c25447b32e31aaf26146d4e8d3bd2cf39070', 'width': 216}, {'height': 240, 'url': 'https://external-preview.redd.it/Lg7Iv25fZ4U0PVveI2CbczY-ZS8IgxSFUe30Ly1O74w.jpeg?width=320&crop=smart&auto=webp&s=cdd2cc83b862f5d7689b6bfe46b07d7540f38c1b', 'width': 320}], 'source': {'height': 360, 'url': 'https://external-preview.redd.it/Lg7Iv25fZ4U0PVveI2CbczY-ZS8IgxSFUe30Ly1O74w.jpeg?auto=webp&s=42de2686ae91a181846365d8f225f9e23058fe25', 'width': 480}, 'variants': {}}]} |
Looking for an open LLM for dark sci-fi roleplay and worldbuilding (less restrictive than mainstream models) | 10 | I’ve been experimenting with free GPT-based models for a while, but most are quite limited by ethical and content filters. I’m not looking for anything extreme or illegal, just something that allows darker or morally complex themes in sci-fi settings—things like the Spartan augmentations from *Halo*, Adeptus Astartes biology from *Warhammer 40k*, or FEV from *Fallout*.
The issue is that most hosted models flag “transhumanism” or combat descriptions as unsafe, even when the content is purely fictional and worldbuilding-oriented. I’d like to explore these ideas freely without the system intervening every few lines.
I’ve seen that Meta’s Llama 3.1 405B on Chatbot Arena can sometimes produce darker, more flexible responses, but results vary. I tried running LM Studio locally, though my laptop (8 GB RAM) clearly isn’t up to hosting large models.
**TL;DR:** Looking for recommendations for open or lightly filtered LLMs suited for dark sci-fi concepting and roleplay. Preferably something free or lightweight enough to run locally. | 2025-10-06T04:47:06 | https://www.reddit.com/r/LocalLLaMA/comments/1nz9y4p/looking_for_an_open_llm_for_dark_scifi_roleplay/ | majorpaleface | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nz9y4p | false | null | t3_1nz9y4p | /r/LocalLLaMA/comments/1nz9y4p/looking_for_an_open_llm_for_dark_scifi_roleplay/ | false | false | self | 10 | null |
The only quantized Sarashina-2-7B using AWQ | 7 | I built the only publicly available 4-bit quantized version of Sarashina-2-7B using Activation-aware Weight Quantization (AWQ).
Sarashina-2-7B is a foundation model from SB Intuitions (Softbank) specialized in Japanese.
I calibrated on the Japanese Wikipedia dataset to reduce the model size from 14GB to 4.7GB while only degrading response quality by 2.3%.
Check it out: [https://huggingface.co/ronantakizawa/sarashina2-7b-4bit-awq](https://huggingface.co/ronantakizawa/sarashina2-7b-4bit-awq) | 2025-10-06T04:42:22 | https://www.reddit.com/r/LocalLLaMA/comments/1nz9v8o/the_only_quantized_sarashina27b_using_awq/ | Ok_Employee_6418 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nz9v8o | false | null | t3_1nz9v8o | /r/LocalLLaMA/comments/1nz9v8o/the_only_quantized_sarashina27b_using_awq/ | false | false | self | 7 | {'enabled': False, 'images': [{'id': '8nPf02LK4oHJMwdU6TXGOQZJ_QsszS5lWgIxDFdxn3c', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/8nPf02LK4oHJMwdU6TXGOQZJ_QsszS5lWgIxDFdxn3c.png?width=108&crop=smart&auto=webp&s=dc1fe0afa16de4709b524b275db12434055c1fe6', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/8nPf02LK4oHJMwdU6TXGOQZJ_QsszS5lWgIxDFdxn3c.png?width=216&crop=smart&auto=webp&s=277119c049c864c5d77dcde3a037e20ff89f7185', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/8nPf02LK4oHJMwdU6TXGOQZJ_QsszS5lWgIxDFdxn3c.png?width=320&crop=smart&auto=webp&s=23eb112c222601b22a8071024542be678d06d0c7', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/8nPf02LK4oHJMwdU6TXGOQZJ_QsszS5lWgIxDFdxn3c.png?width=640&crop=smart&auto=webp&s=a8d11c1cecd75b6e88df2a7dc13561e53dd26ee6', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/8nPf02LK4oHJMwdU6TXGOQZJ_QsszS5lWgIxDFdxn3c.png?width=960&crop=smart&auto=webp&s=a6a66cafbc748e5de864f98299984035b1710f0b', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/8nPf02LK4oHJMwdU6TXGOQZJ_QsszS5lWgIxDFdxn3c.png?width=1080&crop=smart&auto=webp&s=0c429eef5dac26f10e956e95912705b496943302', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/8nPf02LK4oHJMwdU6TXGOQZJ_QsszS5lWgIxDFdxn3c.png?auto=webp&s=fab51b7d182645aacb3fecce3b07f68b287875a4', 'width': 1200}, 'variants': {}}]} |
please suggest some local models based on my specs and also what app to run them in and also explain some other stuff to me please as i am new tho this | 0 | my specs on my gaming pc are the following
7800x3d 64gb ddr5 ram rtx5080 and I am on windows 11
I want to be able to ask general questions and also upload a picture to it and ask questions about the picture if possible
and with my specs what are the pros and cons of running it locally vs using it online like chat gpt or google ai etc.
so far i have downloaded lm studio as I read good things about that in my small amount of research so far but beyond that I don't know much else
also, I am putting together my first nas ever from old gaming pc parts with the following specs
i7 10700k and 64gb ddr4 ram but no gpu and will be using the unraid nas os.
could that do local ai stuff also maybe?
please and thank you | 2025-10-06T03:33:23 | https://www.reddit.com/r/LocalLLaMA/comments/1nz8kqr/please_suggest_some_local_models_based_on_my/ | zeek988 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nz8kqr | false | null | t3_1nz8kqr | /r/LocalLLaMA/comments/1nz8kqr/please_suggest_some_local_models_based_on_my/ | false | false | self | 0 | null |
Qwen/Qwen3-Embedding-0.6B Funciona muito melhor quando query e instruct estão em inglês | 0 | Alguem já notou que o modelo Qwen/Qwen3-Embedding-0.6B funciona muito melhor com query e instruct em inglês? Na própria pagina do qwen eles dizem que dar uma instrução(instruct) a inference melhora significativamente a resposta e de acordo com meus testes é verdade, mas mesmo assim não estava tendo resultados tão satisfatórios, mas quando passei a utilizar query e instruct em inglês as respostas foram muito mais acuradas. Eu acredito que isso ocorre porque o modelo foi treinado principalmente em inglês, alguém notou isso também? Além disso alguma outra dica para utilizar esse modelo? | 2025-10-06T03:20:18 | https://www.reddit.com/r/LocalLLaMA/comments/1nz8bnn/qwenqwen3embedding06b_funciona_muito_melhor/ | Interesting-Cup1811 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nz8bnn | false | null | t3_1nz8bnn | /r/LocalLLaMA/comments/1nz8bnn/qwenqwen3embedding06b_funciona_muito_melhor/ | false | false | self | 0 | null |
UGI-Leaderboard is back with a new writing leaderboard, and many new benchmarks! | 68 | https://huggingface.co/spaces/DontPlanToEnd/UGI-Leaderboard | 2025-10-06T03:00:12 | https://www.reddit.com/gallery/1nz7xdu | DontPlanToEnd | reddit.com | 1970-01-01T00:00:00 | 0 | {} | 1nz7xdu | false | null | t3_1nz7xdu | /r/LocalLLaMA/comments/1nz7xdu/ugileaderboard_is_back_with_a_new_writing/ | false | false | 68 | null | |
Biggest Provider for the community for at moment thanks to them | 2,382 | 2025-10-06T02:17:03 | dead-supernova | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1nz722n | false | null | t3_1nz722n | /r/LocalLLaMA/comments/1nz722n/biggest_provider_for_the_community_for_at_moment/ | false | false | default | 2,382 | {'enabled': True, 'images': [{'id': '6kl3hy76ietf1', 'resolutions': [{'height': 106, 'url': 'https://preview.redd.it/6kl3hy76ietf1.jpeg?width=108&crop=smart&auto=webp&s=18aea362b970217e154337fe374b1ecc4d447d56', 'width': 108}, {'height': 213, 'url': 'https://preview.redd.it/6kl3hy76ietf1.jpeg?width=216&crop=smart&auto=webp&s=d3cf7767792f80aa3067e6494a683970f8bb6d54', 'width': 216}, {'height': 316, 'url': 'https://preview.redd.it/6kl3hy76ietf1.jpeg?width=320&crop=smart&auto=webp&s=f3f4f74f3b9bc0cdd8865bec0b68bc2adb1ef775', 'width': 320}, {'height': 632, 'url': 'https://preview.redd.it/6kl3hy76ietf1.jpeg?width=640&crop=smart&auto=webp&s=86e1c6a42810d36cbc6b71792855914f69ca24a1', 'width': 640}, {'height': 948, 'url': 'https://preview.redd.it/6kl3hy76ietf1.jpeg?width=960&crop=smart&auto=webp&s=1db8baf1e528456700515af705e8771c0f262ba9', 'width': 960}, {'height': 1067, 'url': 'https://preview.redd.it/6kl3hy76ietf1.jpeg?width=1080&crop=smart&auto=webp&s=3ee3f7044bfc87fb142a579ee192f035808442bf', 'width': 1080}], 'source': {'height': 1080, 'url': 'https://preview.redd.it/6kl3hy76ietf1.jpeg?auto=webp&s=8e9f40daec874fdfb5a9736b498d6ee964ef23b2', 'width': 1093}, 'variants': {}}]} | ||
Speed vs. RAM usage for different quant types? | 5 | Hi there, are there any general trends in speed vs. RAM usage for higher and lower quant values? And are there any specific caveats with IQ* quants? If it makes any difference (apart from obviously being much slower) I'm running with just a CPU but plenty of RAM. | 2025-10-06T01:36:58 | https://www.reddit.com/r/LocalLLaMA/comments/1nz67jk/speed_vs_ram_usage_for_different_quant_types/ | Quagmirable | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nz67jk | false | null | t3_1nz67jk | /r/LocalLLaMA/comments/1nz67jk/speed_vs_ram_usage_for_different_quant_types/ | false | false | self | 5 | null |
I have a 12gb ram laptop, what is the best way to run Qwen3 0.6B as fast as possilbe? | 15 | # Qwen3 0.6B is my ChatGPT Pro. Im trying to run it on CPU. I was wondering if i can run 2 or 3 version of Qwen3 0.6B at the same time so that as model1 is answering my question i can ask model 2 the question and so on.? Thanks! | 2025-10-06T01:27:04 | https://www.reddit.com/r/LocalLLaMA/comments/1nz604y/i_have_a_12gb_ram_laptop_what_is_the_best_way_to/ | SnooMarzipans2470 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nz604y | false | null | t3_1nz604y | /r/LocalLLaMA/comments/1nz604y/i_have_a_12gb_ram_laptop_what_is_the_best_way_to/ | false | false | self | 15 | null |
“This is a fantastic question that strikes at the heart of the intersection of quantum field theory and animal welfare…” | 74 | Many current models now start every response in this manner. I don’t remember it being that way a year ago. Do they all use the same bad instruction dataset? | 2025-10-06T00:40:47 | https://www.reddit.com/r/LocalLLaMA/comments/1nz51be/this_is_a_fantastic_question_that_strikes_at_the/ | -p-e-w- | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1nz51be | false | null | t3_1nz51be | /r/LocalLLaMA/comments/1nz51be/this_is_a_fantastic_question_that_strikes_at_the/ | false | false | self | 74 | null |
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