A 2.6B model with tool calling and 128K context now runs at 30 tok/s on a phone
Liquid AI released LFM2.5-2.6B today, and this might be more relevant to local AI than another massive model most people cannot run. The model is only…
Latest
Qwen 3.8 Max improves over Qwen 3.7 Max on the Debate Benchmark: 1462 → 1588. But the average cost per debate increased by 45%.
More info: https://github.com/lechmazur/debate https://preview.redd.it/5rr60v33bfhh1.png?width=3600&format=png&auto=webp&s=bbe9ff2e3c2e599e887a4ec0da7bcc26f71ad4fb This benchmark measures how well LLMs hold an argument under adversarial, multi-turn opposition across a wide range of topics. It rewards broad…
Local LLM 35B MoE — Real-world coding benchmarks (Qwen vs Ornith vs KAT)
I’ve been running a fairly opinionated evaluation loop on ~35B A3B/MoE-class models for coding over the past few months. Not synthetic benchmarks: actual dev workflows, iterative…
Kimi K3 full model running on 16x GB10 cluster at 20+tps
Kimi K3 full model running on 16x GB10 cluster at 20+tps average (llama-benchy coherent corpus) 38tps peak, 750tps prefill. This is the first run of full…
Hugging Face CEO says China is winning the AI race and dominating on open models
This is something that was spoken here and there, and now it is like writing on the wall. The main additional point is that China has…
Introducing Shieldstral. | Mistral AI
submitted by /u/tengo_harambe [link] [comments]
Has anyone tried Mach-1 Additive? 95% of performance of Qwen 3.6 35B while being 10x smaller
Why nobody is talking about this? Seems pretty significant to the community submitted by /u/MuzafferMahi [link] [comments]
Company approved 128GB Mac for research proposal, best model?
I‘m doing a research proposal at my company about running local LLMs to replace daily coding models. Qwen 3.6 27B (or 3.8 potentially) is widely seen…
DeepSeek v4 Flash vs. Qwen3.6-27B, 3.5-122B, and Gemma 4 31B Benchmark
Just wanted to share my agentic coding benchmark run of DSv4F 0731 at both High and Low reasoning efforts (not Max)... I ran a 109-question subset…
A llama.cpp PR caches “hot” MoE experts on the GPU — 33 → 56 tok/s reported with 8GB VRAM
A new llama.cpp PR (#26563) adds a heatmap that tracks which MoE experts are used most often. Instead of keeping every expert on the GPU or…