Search: “benchmark”

Stumbled across this new vision model, SenseNova-Vision. It's a 7B MoT model, Apache 2.0 license, which is cool. The main idea is it treats pretty much all computer vision stuff as just one generation

Muse Glimmer 30B feels significantly more precise and reliable, it almost never drops the ball or breaks rules. However, its designs lack creative depth and richness. Qwen3.6 35B, on the other hand, i

Back after ~6 months — rebuilding my spiking language model around CPU-first inference Hey everyone. It’s been around six months since I last posted anything about this project here. Some of you might

Testing with 3-5 agents. Decode performance is superb, however if one performs a web search and needs to process a few thousand tokens, ALL other agents will grind to a halt: I've tried tuning a littl

Hey everyone, Today we release Luth-2-0.8B and Luth2-2-2B, two non-reasoning models that set a new state of the art for French across a wide variety of tasks for their size 🚀 A few notable scores on F

I've been comparing Cline / Kilo / Qwen Code lately since they all handle long-task state differently. Cline: has Focus Chain, a markdown file kept outside the conversation that gets reinjected on a c

Motif-Technologies is one of the tech company participated South Korea's AI Foundation Model project.(독파모) Upstage(Solar Series), LG AI Research(EXAONE Series), and SKT(A.X Series) are the competitors

Hey. Just tried it on my old ass gpus 😄 Surprisingly Tensor Split is working on 2 gpus almost doubling PP (wonder how it will work with 4 gpus) Q6 — 1 GPU llama-server --model <MODEL_DIR>/Muse-Glimmer

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

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

*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

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

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

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

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 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

Hi all. These are my system specs: dual xeon e5 2696 v2 , 160gb DDR3 ram ECC(1600mhz), 3 gpus: 3060 12gb, p100 16gb, 3050 6gb. And a 400gb nvme sdd RAID0, 3000 mb/s. The model is Deepseek-flash-0731 U

First of all, my setup: Ryzen 9 5950x DDR4 3200Mhz 64gb (2x32) Dual 3090s, no NVLINK Runtime: llama.cpp Nvidia Drivers 610 Windows 11 25H2 Qwen 3.6 27B Q8 I've been using llama-server with --split-mod

NVIDIA Nemotron Parse 2.0 transforms document images into structured, machine-readable representations with text, layout classes, bounding boxes, and reading-order information. Given a Red, Green, Blu

https://github.com/yhfgyyf/vllm-deepseek-v4-sm89 I couldn't believe that someone actually got vLLM working with this particular set of GPUs, but here it is. The video is from right after I got it work

Prime Agent is an open-source coding and research agent for general and long-running work. A self-improving RLM harness for coding and long-running autonomous tasks. Designed to be both token-efficien

AI9Stars has released G9v3-39A5B an open weights language model designed to deliver even stronger reasoning capabilities than ai9stars/G9v3-3B with its 39B and 5 active experts. It is released under t

I know qwen 3.5 4b is great but a bit too large and miniPCM5 1b is great for agentic use but not so great for multilingual natural language understanding. Google eXb variants are just too big in total

You have two choices here (in order of pref): Downgrade CUDA from 13.3 to 13.1 (skip 13.2 due to bugs) <- prefer this (thanks to u/fairydreaming for pointing this out) Use this vibed fork that works w

I deployed K3 on 32 H100s at work a couple of weeks ago and then got annoyed that there was no way to poke at it on my own machine. So I wrote an inference engine for it in C99. Nothing clever going o

# Running DeepSeek-V4-Flash-0731 (155 GB MoE) on a DGX Spark with vLLM-Moet 2-bit quantization I used Deepseek-v4-Flash-0731 cloud API settig up vllm-moet to run deepseek-v4-flash with MTP locally on

So upfront I'll admit that this is a ChatGPT summary of my chat with it about this idea i had, but this post wouldn't exist any other way, so... I’m trying to get a better understanding of how VRAM is

Been trying to find something that actually handles my workload well instead of just being 'fine.' Started on Qwen 2.5 7B, moved to Qwen 3 8B, and right now I'm using Nemotron 3 Ultra (the big 550B on

I'm guessing 3 years, what do you think? In other words: many of us will be able to afford a general purpose robot in 3 years to experiment with in the home. Cost roughly $5k? Probably small size, but

Hey everyone, ​I recently finished pre-training BetterGPT-150M, a small, lightweight causal language model with ~152 million parameters.Trained on 15B tokens. Dataset & Training: Trained across stable

The Test Bench Setup I tested running a massive 1.56TB Mixture-of-Experts (MoE) checkpoint (96 shards, 93 layers, 896 experts/layer, ~4.46 bits/param MXFP4) on a budget gaming laptop. Laptop: HP Victu

Mage-VL is a codec-native, proactive-streaming multimodal foundation model for image and video understanding, whose visual encoder is trained entirely from scratch at a compact 4B scale. It targets a

I was very curious why all the models were topping out on GPQA-Diamond around 92 or 93% (AA) and spent the last few weeks pouring over GPQA (Diamond and Extended), and then expanded to auditing MMLU-P

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 &

I've been building Krasis, an MoE-focused runtime for streaming big models through limited VRAM on NVIDIA consumer/workstation GPUs, and I think this is the most interesting result so far: Ornith-1.0-

Everything runs through WebGPU, in-browser or in electron/tauri apps. It's fully portable and supports either Nvidia and Apple Silicon (Metal). The actual kernels are optimized for the specific hardwa

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

Quantized Krea 2 Turbo checkpoints for ComfyUI, up to 2x faster and about a third smaller than the usual FP8 version — no calibration dataset, no quality cliff. How to use it (short version): clone th

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.

I am currently running Qwen3.6-30B in Ollama, through Cline to use as an agent in VSCode. Qwen's skill in coding is not in question, but the performance in VSCode is slow and inaccurate and times out

LLMs handle speech well once you run speech-to-text. They don't hear the rest: a bird outside, a glass breaking two rooms away, a smoke alarm two floors down. I've been working on an experimental open

Below Upstream Status sections are from https://github.com/PrismML-Eng/Bonsai-demo Upstream Status for Binary Q1_0 is supported out of the box in upstream llama.cpp across many backends: CPU (generic,

What it is: ATH-MaaS/OvisOCR2 - a 0.8B document-parsing VLM post-trained from Qwen3.5-0.8B (SFT + RL + OPD), Apache 2.0, runs on vLLM 0.22.1. One prompt per page image -> complete markdown (HTML table

Introduction RxBrain (Hy-Embodied-RxBrain-1.0) is a unified multimodal foundation model for embodied cognition — a single model that couples language reasoning with visual imagination to deliver three