Search: “benchmark”

Announcement: https://www.tbench.ai/news/terminal-bench-4-0 Leaderboard: https://www.tbench.ai/ Imo the best aspect in their announcement is their focus on rapidly iterating on TerminalBench to keep t

I wanted to see whether NInfer could run Qwen3.8-27B on an RTX PRO 4000 Blackwell 24GB. Hardware/software: NVIDIA RTX PRO 4000 Blackwell, 24GB 145W power limit CUDA 13.3 NVIDIA driver 610.x Ubuntu LXC

We're releasing Qwen3.8-27B quantized with our newest methods, GSQ + RCO. Higher-quality models, same file size, now with the search and the quantizer both learned. What's inside: GSQ (Gumbel-Softmax

Megathread for discussing the (impending) release of Qwen 3.8 Flash Next. Quants Fine-Tunes & Abliterations Chat Templates Inference Server Support & Configuration Experiences, Benchmarks & Model Comp

we made vision mlx quants of qwen3.8 27b (9 builds from 8bit at 29.5 GB down to 3.23bpw DWQ at 11.8 GB) and compared them against other community vision mlx quants from hf (we only compared vision bui

I ran 11 uncensored variants of Gemma 4 12B that I grabbed from huggingface, sorting by downloads. 10 full abliterations plus 2 LoRA adapters which were requested to be added in the comparison, agains

TL;DR - A ~4M example, ~3B token CoT dataset designed around helping small models think more concisely, accurately and reliably. Hi all! For the past few months I have been working on a dataset design

Share what your favorite models are right now and why. Given the nature of the beast in evaluating VLMs (untrustworthiness of benchmarks, immature tooling, intrinsic stochasticity), please be as detai

Standard constrained RL assumes consequences are immediate and attributable to the current action. This breaks down whenever violations are delayed and stochastic, which is most real-world settings yo

If you’re trying to run local LLMs on a normal laptop, an older desktop, integrated graphics, limited VRAM, or simply the hardware you already own, r/LowEndLocalAI is meant for you. The idea is simple

Try it here: https://ocean.blackbeardlabs.dev/ Model: Qwen 3.8 27b Q8_X_KL Unsloth Hardware: 3 x RTX3090 Harness: DeepSeek Harness Prompt: /goal I want you to create a **JavaScript + Node.js WebGL pro

Qwen3.8-27B is amazing, but it’s slow. A stronger 35B-A3B Mixture of Experts-coder that can run and solve real codebase issues fast (even on constrained hardware) is a valuable addition to the arsenal

My setup: RX 6700 XT 12GB i5-11400F 16GB RAM Ubuntu 26.04 ROCm/TheRock + llama.cpp I've spent the last few months experimenting with different backends, quantization, CPU/GPU split, KV cache, batch si

Hello, So I've been trying lots of combinations in that never-ending landscape of options and settings. I wanted a proper quant of 3.8 27B running as fast as possible on my 5090 at 400W, with vision a

Hi everyone, I'm currently working on optimizing an LLM setup at my company and I'm trying to identify where the actual performance bottleneck is. Our current setup is roughly: LLM: NVIDIA Nemotron mo

When using DeepSeek Pro on Ollama, you'll see that the underlying cost is more significant. But if you actually track the usage, you'll actually find that, at least in my benchmarks, I save a lot of t

I'm introducing Aurora-80K, a small language model with exactly 80 thousand parameters. It uses a factorized 4,096-token vocabulary despite having only 80K parameters. The benchmarks: Wikitext-2 BPB:

IQ quants are particularly slow on CPU at large batch sizes (what you'd see for imatrix and perplexity) Benchmark numbers I ran PPL against master and this PR to get speed and numbers on --chunks 50 f

I benchmarked Qwen3.8-27B on MathArena/aime_2026 dataset, comparing BF16 and FP8 weights at medium and xhigh reasoning effort. Interesting findings are: quantized FP8 xhigh is better than BF 16 medium

Like many of you I've spent the last few days throwing Qwen3.8-27B against all of my usual use-cases and personal tasks/harnesses and workflows. It's great, phenomenal sometimes, but that's not what t

(my own experience) I have a mid-range gaming laptop from 2024 with 4GB VRAM and 16GB RAM. There is nothing that I can run other than small models at humble t/s. I mostly use the models to generate sc

I was checking out benchmarks of this model and apparantly better than Qwen3.5 9b reasoning across the bench on artificial analysis. I have used the 9b model for variety of stuff and it has been amazi

Including the rationalisation for the data below - this is a more robust version of an earlier post I did similar to this - explaining below: How I chose the comparisons The basic question I’m trying

If you scroll down from the countdown at https://huggingface.co/Qwen/Qwen3.8-27B, you see a big model card with a bunch of sections: Highlights, Model Overview, Quickstart, Best Practices, Citation, e

Ive been experimenting with task-aware GGUF quants for months, taking inspiration from TASA and TAQO but pushing the allocation lower to to the tensor level. The basic idea is to generate a custom ima

Hello everyone! ​I like dumb things. I like working with weak computers and microcontrollers. I like the simplicity and low electricity usage. Simply put, the efficiency of a 'dumb' PC. ​The first tim

Spent two nights getting deepseek-ai/DeepSeek-V4-Flash-0731 (284B MoE, 13B active, native FP4/FP8, 1M context) running production-grade on two DGX Sparks connected by one QSFP DAC cable. Everything —

Hey everyone - we've been building something particularly relevant to ML at large - The Agentic World Cup - a platform where Agents compete in sports. As you know, today's Agents can code, do math, an

Benchmarked Muse Glimmer 30B on my RTX 5090 (32GB), 262k context, UD-Q5_K_M + dflash-kquant + mmproj. Workload Stock master + DFlash ngram-simple PR #26842 + DFlash Code patch 78 t/s 57 t/s 220-253 t/

Wowee!! Just when you thought it couldn't get better for open weight models, we probably have had our best period yet!?!?! Models that rival the closed frontier, Opus level models on non-insane hardwa

Hi r/LocalLLaMA 👋 Today we’re excited to release Muse Glimmer, a 30B open-weight model built specifically for local agent workflows. We’re releasing the weights to the community under a permissive Apa

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

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

(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

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

I'm the author, so discount the enthusiasm accordingly. This is an unaffiliated community port, not endorsed by the vLLM project, which it uses to verify its correctness. What started it: I love vLLM,

I built this because the existing benchmarks were using random data and with MTP content types can vary a lot on what performance you see. 5% or more with content types. BetterBench is designed to hav

Inspired by a post from u/giveen I motivated claude (no patinence on my side to work through everything myself) to help me get DS running on my MacBook M5 Pro 64GB and it exceeded my expectations.. be

Qwen3.8-Max (2.4T) is another massive contribution to the open weight community. On benchmarks, it performs closely to Kimi K3 and DeepSeek V4 flash across all categories and is better at coding and s

Spent way too much time with V4-Flash-0731 this weekend and wanted to share my vibes as briefly as possible. I sent it through a bit of real-work and some of my personal benchmarks. My quick thoughts

I decided to test a few questions against DeepSeek-V4-Flash-0731. Locally, I was running Unsloth's UD-Q2_K_XL quant. After I saw the surprising shape of the results, I tested against DeepSeek's offici

I love the progress happening on open models but I feel like it is kind of getting clear that hardware to run good sized models is completely unaffordable for me right now. I know that you all love Qw

We all have been there, tinkering around with models is fun but we rarely do it with research precision and issues are often subtle and hard to reproduce. There are a lot of benchmarks but running the

GPT-Live is so good that I use it almost every day. I've been wanting to replicate it since it was released. My first attempt was to fine-tune Gemma 4 12B to behave like a full-duplex model. Something

March 6th, 2026 the highest intelligence index score was 51 for frontier models. deepseek-ai/DeepSeek-V4-Flash-0731 that has an intelligence score of 50. If these benchmarks are accurate, models avail

SenseNova released U1.5-Lite-Preview Benchmarks: Qwen-Image-Bench from 47.14 to 55.20. ImgEdit-Bench from 3.90 to 4.37. GEdit-Bench-en from 7.47 to 8.17. Key updates: 4K native generation with better