Nace AI Open-Sources Drex Max v1: A 27B Decision Model That Runs Locally on Apple Silicon

📰 Nace AI Open-Sources Drex Max v1: A 27B Decision Model That Runs Locally on Apple Silicon Nace AI open-sources Drex Max v1, a 27B multimodal decision model scoring 67.52 that runs on Apple Silicon. 📰 Source: MarkTechPost 🔗 Link: https://www.marktechpost.com/2026/11/01/nace-ai-open-sources-drex-max-v1-local-inference/ # AI # ArtificialIntelligence

hey llamas, we open-sourced Drex-Max v1 at Nace.AI. it's a dense 27B multimodal decision model, released under Open RAIL-M. the main difference from a normal LLM is that it doesn't generate anything. no tokens, no JSON to parse. you give it a context state (text, JSON, high-res images) plus one or more named questions, and a 255-way readout head returns probabilities over all the candidate answers in a single forward pass. It runs on NVIDIA GPUs through Python, and on Apple Silicon (Metal) and CPU through our llama.cpp and Ollama forks. latency is around 400ms. Architecture notes no decode loop. one forward pass, and the last hidden state maps straight to probabilities for up to 255 options (via readout.safetensors ). 64-layer hybrid backbone. 48 Gated DeltaNet linear-attention layers (cau

https://preview.redd.it/260fkihtk3zh1.png?width=2400&format=png&auto=webp&s=a80b6861abdeee3c9a4e705b6ad9b337be005722 hey folks, I'm Ye, one of the contributors to Kapitsa, an open-source inference engine optimized specifically for Apple Silicon to run Qwen, Nemotron, Flint and Clover models locally. Our custom-written Metal kernels let us tune for each chip and each model. https://github.com/nace-ai/kapitsa/ The latest update, 0.2.26, is out. Since our last M5 Max numbers, Flash-Next 180B reads prompts 46% faster and the 27B writes 8% faster, and DiffuSpace's Clover 35B joins the catalog. Kapitsa also runs Qwen Flash-Next 180B with SSD offloading : on a Mac that can't hold it, the experts that don't fit in RAM stream from the SSD, so it runs on a 48 GB MacBook Pro at 61 tok/s. Here are the