Knowledge catalogue
Demand for low-precision inference, including NVFP4-based approaches, has grown as large language models are increasingly deployed in latency and cost constrained production environments. Quantization
The past week I've been running DSv4 inference on my laptop by keeping everything RAM-resident except the MXFP4-experts (since expert pool is ~147GB and won't fit) TL;DR - read speed is the limiter mo
Available context length with and without the patch: Model: QWEN 27B ROCm stock patched Vulkan stock patched IQ4_XS Pure, single 16GB GPU 19.456 76.032 68,352 78,592 Q6_K_L on 16GB + 12GB 64,256 149,2
What Local Embedding + Reranking Models are you guys running for RAG? I went down this rabbit hole because I wanted a Embedding Model + Reranker for a Translation Memory Server. Essentially, given X p
Disclosure: I’m the author of Ante. DeepSeek recently reported an 82.7% score on Terminal-Bench 2.1 for DeepSeek V4 Flash 0731. Its evaluation used “DeepSeek Harness minimal mode,” which hasn’t been r
After seeing the benchmark results for the full release of DS v4 Flash 0731, I replaced my 2 x 16GB DDR4 ram sticks with 2 x 32GB DDR4 ram sticks to get a max supported of 128 GB RAM, in hope to be ab
Hey everyone, I've been experimenting with the new DeepSeek-V4-Flash-0731 release locally using the Unsloth Studio Q8_K_XL GGUF with OpenCode. Overall, it's been working really well, but I've noticed
table bench https://huggingface.co/bartowski/endless-frontier_BigBang-v1-GGUF I'm downloading this model only because Bartowski converted it to .gguf, so it might be interesting. Doubts : The headline
First of all, I'm not a lab, this was a solo summer research project that finally culminated into the github repo and the writeup. The repo includes a much deeper dive with methods, findings about qua
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
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 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
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
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
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 =
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
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
Hey peeps. I know you're tired of low quants giving hard to believe numbers. I'm quite skeptical too and from what I tried I'm often left with the impression that the claims fall short. So this model
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
LabyrinthBench measures the thing that actually kills long agent runs — whether a model can still use what it learned twenty turns ago — deterministically, with no LLM judge, on your own hardware, wit
LFM2.5-2.6B is a new tiny model by LiquidAI, with benchmarks that put it head to head with much larger models. I've run llama-perplexity on many model GGUF quants, crossed with many KV cache quants, t
A fresh llama.cpp PR (#26689) changes what looks like a tiny SYCL FlashAttention dispatch decision. With a quantized KV cache ('q4_0' / 'q8_0'), decode was being sent through the VEC kernel. On the au
I swear AA is not the bipartisan they so claim. An open source mode (Qwen 3.8 max) was number 1 on the agentic index, then they just so happen to launch 'v4.1.1' of their index in which they just adju
High-performance inference of NVIDIA's Parakeet TDT 0.6B V2 English transcription model, in the browser. Check out the live demo: https://parakeet.narcotic.sh/ A fully custom, dependancy-free implemen
I'm considering replacing a single RTX 3090 with two ASRock AMD Pro R9700s for about 2900 new out of the door. That would move me from 24GB to 64GB VRAM. Yes yes, CUDA/ROCm, but the real problem is po
I run the following on a 5090 and have been okay with its performance, it does most things somewhere 80-100 t/s, though that can slow down at full 262k context - more like 40 t/s at times. I use it pr
Hey all, I'm serving DSv4Flash 0731 on a cluster of 2x DGX Sparks but am running into constant issues with having almost no RAM (unified memory) left for the OS/cache and I'd love to hear the communit
📝 Introduction We present Wan-Animate-2, a novel end-to-end character animation framework that directly consumes driving videos in a redesigned Diffusion Transformer, which achieves high-fidelity moti
For a long time now, the most popular posts on LocalLLaMA have been either about using LLM in the cloud or about politics. I suspect that people using local models are about 10% now. You can say that
Following up on yesterday's post about running everyone's faves on 2 x 16gb cards while maximizing performance and KV. Previous post data used abandoned Cu130 VLLM image. Stats here are done on cu129-
TL;DR: On a Qwen3.6-35B-A3B Q6 setup sized for 64K context on a 24GB RTX 3090, spilling eight MoE expert layers to CPU freed enough VRAM to increase -b from 512 to 1024 and -ub from 128 to 512. Prompt