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When I released Inflect v2 last week, I thought most people would ask whether a TTS model this small actually sounded decent. Instead, I kept getting two questions: “Can I train it on my own voice?” “Can I move it to another language?” At the time, my answer was basically: technically yes, but not w
submitted by /u/BritishDudeGuy [link] [comments]
tldr; we are going to host K3 on A100s (yes, thats correct, we'll try to see if it holds up), H200s & B300s - expect results for A100s & H200s this week while we setup the B300 cluster this weekend & maybe results by next week. Weights are supposed to hit Hugging Face today (Moonshot committed to Ju
submitted by /u/pmigdal [link] [comments]
Hey r/LocalLLaMA, I’m a retired engineer with a background in distributed computing, currently running a 1-person startup. Like many people here, I’d love to experiment with 2T+ MoE models locally. The problem is that I don’t have an H100 cluster in my living room. So I’ve been thinking about a fai
https://preview.redd.it/k97l56d8ypfh1.png?width=606&format=png&auto=webp&s=2a2e2156bea56b25f5709e8f1df2bf82525eb089 https://x.com/alexandr_wang/status/2081501627836661928?s=20 submitted by /u/External_Mood4719 [link] [comments]
Every time a new model is released, I tend to check it out. The benchmarks, readme, or whatever seem pretty convincing, so I download it, test it for a few hours, and then I just go back to the same couple of ones I already had. Curious what models people here have actually kept using for weeks, mo
I got tired of my Ollama server sitting idle between chat experiments, so I pointed it at my Navidrome library and made it run a radio station. The DJ is an agent, not a shuffler. Each turn it gets tools (search the library, look at what played recently, check the schedule, read the weather) a
I was refreshing my youtube and found out my favourite reviewer uploaded a battery test of 78 smartphones: https://youtu.be/MpgUFrsIWSQ the author said they started using robotic arm to simulate a person using the phone but they wanted to further enhance it by using agentic ai. cloud ai have too hig
submitted by /u/Unusual_Guidance2095 [link] [comments]
I ran DeepSeek V4 Flash through Claude Code, OpenCode and Pi on my own benchmark, and the quality came out basically the same across all three while the time and tokens spent was wildly different. Claude code (with DS in CLIProxyAPI) takes nearly 4 times longer than the fastest to land the sam
clem 🤗 on 𝕏: https://x.com/ClementDelangue/status/2081056675558195657 • Radical transparency: let’s release the traces from the “rogue” agents so the entire research community can study what happened. • More capabilities for defenders: let’s commit $100M in compute from OAI to help the Hugging
Kimi K3 is supposed to get open weighted tomorrow! Can't run it or even a model a hundred times smaller lol, but its still a great win for open source. For me, personally im more awaited for the new inference providers that will open up hopefully. https://preview.redd.it/ix64yipudkfh1.png?wid
After a long and grueling effort spearheaded by ngxson, llama.cpp now fully supports MCP for all protocols. Over-the-web HTTP servers were already supported in the client (since they don't require any sort of plumbing), but stdio servers required real integration. After we modified the `llama-cli` t
3 year lurker, now i finally got my server up and running. dont know which model to choose. llama.cpp or vllm, what makes more sense? mainly single user with maybe 2-3 more additional users in family, if everything checks out. hermes is gonna be used as "ai playground" to manifest ideas on tailscale
I understand that the laguna model is either still buggy or potentially benchmaxxed. So I’d like to know for people who really tested, are DS flash or Hy3 really better in your usecase? submitted by /u/Leflakk [link] [comments]
I'm looking for a model to chat with, reasoning, maybe get some career or life coaching. I don't care at all about multimodal or coding ability Just it's intelligence in remembering context in a conversation or a specific topic, thinking out of the box, etc. Must fit in 128gb, if it matters to perfo
I've spent the last year or so playing around with open source MLX and GGUF models on iPhone hardware. Given the limitations in memory, GPU/CPU/ANE, and in turn the context window I've been trying to figure out the best use cases for them. I've also done a lot of testing with Apple Foundation model.
Just noticed this exists, 1M context MOE with 48B par seems just like what Ive been looking for - it runs pretty damn fast too compared to Qwen 3.6 35B. after some testing it seems capable of producing *not terrible* results but it always tries to go for the minimun possible output that could
Cuda and Vulkan Benchmark: TensorSharp vs. llama.cpp I would like to share my latest open source local Unsloth (GGUF) LLM inference engine and applications. It supports many models from Unsloth, like Gemma4, DiffusionGemma, Qwen3.6 with multi-modal (image, vision, audio), Qwen Image Edit, reas
submitted by /u/hongnoul [link] [comments]
Please be honest. I would love to hear about guys really dedicated to local AI and who really reject subscriptions (especially to openai and anthropic). What do you use your model for? submitted by /u/takoulseum [link] [comments]
Hi everyone! Over the past five months I've been working on DKV (DifferentialKV), an open-source project exploring KV-cache compression for long-context local LLM inference. The goal is to reduce KV-cache memory requirements through anchor-based representations, joint low-rank compression, exa
https://huggingface.co/amd/Instella-MoE-16B-A3B-Think I was browsing HuggingFace and came across this model apparently uploaded a day ago, and thought to share it here. I've not tried it out yet, but it's good to see AMD joining the open source model game. submitted by /u/Look_0ver_There [li
https://reddit.com/link/1v5rvuq/video/bgmwc754i9fh1/player My goal was to create a benchmark to measure the spatial awareness and memory of models. Eventually, I came up with the simple idea of a maze where the model must find a key and use it to open the escape door. Here’s the difference to
Being excited about a new 120B-class model, I decided to test it on a problem that took me a few days to solve. The problem is to rearrange the data from one representation to another but do it within a fixed memory budget without dynamic allocations. The latter condition makes it difficult to decom
Hello all, there are so many great models coming out right now that it is difficult to keep up with their architectural differences, and especially their attention designs and innovations. I know about several of the most important models, but I was curious about a long list of interesting rec
Im not telling this model good or bad. Im just wondering how they passed benchmarks if their templates and many other things was broken? And it took some time to fix that (so they haven’t had the right one laying around I suppose?) not just they uploaded “wrong” files Maybe their benchmark numbers
I’m not affiliated with this project, but I’ve been running it recently and I’m surprised it hasn’t received more attention here: https://github.com/fewtarius/CachyLLama CachyLLama is a fork of llama.cpp focused on a problem that matters a lot on slower hardware: repeated prompt processing. Not only
A swarm of GPT 5.6 Sol agents spent over 40 hours optimizing a Kimi K3-like model from 65 to 406 tok/s. This animation follows their collaboration as they discover operator fusions, transform the execution graph, and develop new kernel algorithms. We start with a fully decomposed 331-node Ki