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Hi folks, I hate slop as much as you do, so instead of starting with 'The Problem', I'll just cut to the chase: I just published Lophius, which is the culmination of more than two years of fighting wi

Hey everyone, I constantly see high praise for M3 and M4 Macs for local LLM inference, even the base/16GB models. However, my experience has been quite different, and I'm trying to figure out if I hav

I have a Xeon w7-3465 and 4 sticks of RDIMM DDR5-4800 with a theoretical max bandwidth of 153GB/s. I am trying to run DeepSeek-V4-Flash-0731 as it is an MoE and the weights are in MXFP4, so I should r

*SupraLabs released a new experimental model!* SupraElegans-500K is a ~500,000-parameter causal language model built around a sparse, signed, recurrent neural graph. No Transformer, no attention mecha

I am not a meteorologist, but I just read a very interesting article: https://arstechnica.com/science/2026/08/deepminds-hurricane-model-bought-forecasters-an-extra-day/ In a paper published on Thursda

H3 weights went live on HuggingFace August 3rd and I started pulling them immediately. An omni-modal video model with native stereo audio in the same forward pass, where audio can actually drive the v

Tweet by u/hackerllama Could be copium, but I would love to see Gemma 4.1 there with unified audio input for all model sizes perhaps even up to 120B, much improved tool calling (even with the latest t

Would it be possible and make sense to add metadata to training data e.g. a trustfactor (0.0 - 1.0)? For example: the older data is the less trustworthy it is. And data after 2022 gets less trustworth

There are so many posts where people complaining about high prices and asking for solution <= 1000 EUR. So, there is one solution to consider: PC/mini PC/laptop on Ryzen 7 260/Ryzen 9 8945HX/etc CPU w

Howdy - I posted a benchmark here - https://www.reddit.com/r/LocalLLaMA/comments/1vbtiy7/deepseek_v4_flash_on_slopcodebench/ This was using the hosted API - since then I've been playing around with qu

I have various quants of this model and am curious how they perform. can anyone recommend which benchmark would be a good test case for quantization effects? Maybe that can be completed with about 1 m

I’ve been using 3.6 27B Q4, and that quant is fast on an M5. The code has been average, but consistently “good enough.” And, after a year, I can see home LLMs being served at home much like streaming

I’m planning a dedicated home AI server, mainly for local LLM inference, agents/tool use, Docker services, and eventually larger MoE models with CPU offload. My plan is to start with 2x 16GB GPUs = 32

Over the past few months I have been building a CPU-first inference engine from scratch in pure C99 (no Python, no CUDA, no BLAS, just GCC and make). The focus has been running 1.58-bit ternary models

I was wondering what a minimal coding agent implementation would look like that can be used like Claude Code or Codex Not feature-by-feature of course but basically stripping everything out that is no

I’m running DSV4F 0731 on dual spark, and honestly… wow. It’s an absolute workhorse, and the benchmarks are real. Everyday tasks with Hermes agent? Effortless. Coding tasks with OpenCode? I’m genuinel

Disclaimer - no LLM was used to write this post/note As larger post about my setup will come later, want to give heads-up to folks who use VLLM and >= 2 GPUs. So I have pretty meaty server (8 channel

Hey everyone, I could use some advice on setting up speculative decoding correctly with llama-server. My Hardware: GPUs: RTX 4090 + RTX 6000 Pro (120GB total VRAM) RAM: 32GB I am currently testing the

I discovered recently that Google uses their own TPUs, like tiny ASIC cards like the toy ones that existed for bitcoin. And while it sounds inefficient the fact they use thousands of them because...th

I’m building DesktopLab, an open-source local-first control plane for development agents. I recorded the setup boundary that most agent demos skip: DesktopLab detects the host, proposes the supported

What IDE are you using. its another problem area for me . I usually use VSCode , but with local llms I have not found an extension which works optimally VSCode CoPilot chat with Ollama: CoPilot bloats

(I am not a native speaker, written by myself, so please bear with me) I really want to like DeepSeek-V4-Flash-0731. But it has serious flaws that don't align with the high score on intelligence bench

Firstly a big thanks to the poster 'hellohazine', he basically only removed the multi-lingual fat of the model and just kept the English language intact. It is the exact model, and the rest of the mod

seems to be about as good as a vega 56 with 16Gb of VRAM, is it worth it? (don’t want to deal with NVIDIA drivers on Linux, already have an rx6650xt and might simply use vulkan for llamacpp inference)

This PR should be ready for testing now. I tested with a very small (8B params) sub-model extracted from the original one. Appreciate if someone can test with the bigger model. GGUF(for testing) from

Running across 2 clusters using llama.cpp over RPC too. Both clusters are not enough to hold everything in memory, so main cluster still partially offloads to run. Goal will be to get all the GPUs in

I am wondering if anyone can give opinion on if Ollama Cloud pro or max plans are worth it. Id be looking to use it with Kimi K3, Qwen 3.8 and Deepseek v4flash for now. Wondering if it would be better

Basically what the title says. For me, it would always crash and burn trying to use tensor split. Apparently, there's some bug where GPU memory gets corrupted with the default microbatch (512) or high

Hi everyone, I'm a systems analysis student researching a problem a lot of you probably know well: how much you actually trust the published VRAM/RAM requirements for open-source models before trying

I compared Qwen 35B-A3B MoE against Qwen 27B dense on a series of local coding-maintenance tasks. On my R9700/llama.cpp setup, the MoE model generated about 3.9× faster (~116 vs ~30 tok/s), but the co

I've been tuning my new Radeon AI Pro R9700, and figured that this would be useful information for people who are trying to optimise their setups. I'm pretty happy with these results and looking forwa

Not the biggest or shiniest, but it's mine From gaming machine inference on the original llama models, to a 4x RTX 6000 Pro Max Q + 4x 3090s local AI cluster. Pictures are in reverse chronological ord

MiniMax-H3 in ComfyUI 0.30.0, RTX 4080 16 GB (224 W cap). int8 DiT + int8 Qwen3-VL-32B text encoder. Turbo LoRA @ 0.9, euler + simple, 6 steps, no CFG. MiniMaxH3ReferenceToVideo with 3 reference image

Looking for V100 users to share your config and it's performance. GPU: Tesla V100 PCIE 32Gb Qwen3.6 27B Q4_K_M + Q8_0 MTP 128K context length Pi coding agent llama.cpp model preset: [*] spec-default =

Build a 100% offline fast Retrieval Augmented Generation (RAG) system that runs without an internet connection, without cloud APIs, without OpenAI/Ollama Published a video where you can build a fully

Follow-up to my original Spectrum MiniMax H3 post: https://www.reddit.com/r/StableDiffusion/comments/1vf1ze3/spectrum_acceleration_for_minimax_h3_in_comfyui/ In that first post, I released the MiniMax

I was going through the current llama.cpp CPU PRs and #26348 stood out because this isn't the usual +5% kernel optimization. It adds an x86 VNNI implementation for the Q2_0 × Q8_0 dot product, and the

I have not been successful with management to get funding for local resources despite bringing forth solid arguments about data sovereignty and related architectures. What actually succeeded in gettin

Or is there any reason why I feel like model output quality seems to be better when I use higher micro-batch values (ub) in llama-cpp? I don't really have any hard numbers or anything (just running th

Press My earlier prediction that Tesla would buy them completely missed the mark. With AMD focusing heavily on the enterprise side, the idea of consumer-facing hot-swappable AI model chips looks prett

Is anyone here successfully running DeepSeek-V4-Flash-0731 locally with vLLM, especially on AMD MI325X? My setup: GPU: 1x AMD Instinct MI325X Model: deepseek-ai/DeepSeek-V4-Flash-0731 vLLM: 0.26.0 ROC

today was the last day of my subscription on ollama cloud, to be honest it was a great price value for me and with GLM 5.2 and Deepseek V4 Pro i was able to Vibe code my custom woocomerce shop with mu

Code and instructions available here: https://github.com/albertoZurini/echo-dot-2-playground Hello there! After a few days of experimenting I was able to get a completely local voice pipeline running

Hello, For the past few days I have been benchmarking Gemma 4 26b QAT UD Q4_K_XL extensively versus Bartowski's Q4_K_L. While QAT is certainly very effective and reducing memory consumption versus the

Hey everyone, I just wanted to share my journey here for some motivation. Three years ago, I saw the sudden spike in AI and realized it was the future of tech. My goal at the time was to be an indie g

There are already plenty of different extensions for voice input, but all I found required having a second server running. I wanted something super simplistic: launching local STT server just for my p

Since now we have kimi k3 and next week we are getting Qwen 3.8 Max and also soon V4 pro Deepseek. I am curious if the old power house like Kimi 2.6/7 code and GLM.5.2 are all that relevant. especiall