Tag
Holo3.1 is an updated computer-use model family that improves robustness across web, desktop, and mobile environments, introduces quantized checkpoints for local execution, and adds native support for function-calling protocols.
NVIDIA releases Qwen3.6-35B-A3B-NVFP4, a quantized version of Alibaba's mixture-of-experts multimodal language model, optimized for deployment on NVIDIA GPUs using Model Optimizer.
DealignAI releases CRACK-abliterated and MXFP4/MXFP8 quantized versions of Qwen3.6-27B and 35B models, preserving MTP for faster speculative decoding on Apple Silicon.
Qwen 3.6 27B runs fast on 16 GB VRAM thanks to 'Pure Quant' technology, achieving 40 tokens/s with MTP and supporting 64k contexts, enabling local AI on consumer GPUs like RTX 4060 Ti.
TurboQuant is a GGUF quantized version of the Qwopus3.6-27B-v2 model, confirmed with GPQA test results and shared on Hugging Face, with credits to Jackrong and KyleHessling.
Release of Qwen3.6-27B-PRISM-PRO-DQ, a dynamically quantized GGUF version of Qwen3.6-27B with bias/propaganda removal, preserving native MTP draft head and vision tower, enabling lossless speculative decoding for faster inference.
CohereLabs releases Command A+, an open-source 25B active parameter model optimized for agentic, multilingual, and reasoning tasks, with vision support and Apache 2.0 license.
A new 18B merged quantized model, Qwopus-GLM-18B-GGUF, outperforms 35B MoE models while using half the VRAM and running on consumer GPUs.
Google’s Gemma 4 E2B/E4B quantized variants now run fully offline on iPhone via apps like Locally AI, leveraging the Apple Neural Engine for on-device inference.
SuperGemma4-26B-Uncensored-Fast GGUF v2 is a quantized, locally-runnable variant of Google's Gemma-4-26B model optimized for Apple Silicon, offering faster inference speeds and less-censored chat behavior while maintaining practical performance on general tasks.
SuperGemma4-26B-Uncensored-MLX-4bit-v2 is a fine-tuned and quantized variant of Google's Gemma 4 26B optimized for Apple Silicon, offering improved performance on code, reasoning, and tool-use tasks while maintaining faster inference speeds compared to the stock baseline.