38 summarised stories about Model Compression, each linking back to the original source. Browse all topics →
TechCrunch 3 weeks ago 50 ● 4 sources
PrismML released Bonsai 2 27B, a compressed reasoning large language model designed to run on PCs and smartphones. The model compresses Qwen3.8 27B down to 5.9 GB, matching 98% of Qwen’s aggregate benchmark scores. As a result, LLMs can be deployed with much lower memory use while aiming to preserve performance, with the company planning next releases targeting several-hundred-billion parameters.
Simon Willison’s Weblog 3 weeks ago 46 ● 4 sources
A Hacker News commenter explains how to run the Bonsai 2 27B GGUF model using Prism’s llama.cpp fork and provides local setup steps. The GGUF download is about 5.95 GB. Users can launch a local server (with a warning about the tensor API not being supported in their environment) and see token throughput that varies between restarts.
The New Stack 3 weeks ago 11
Intel researchers introduced BITCOS to compress ternary LLM weights by storing zero locations separately, without retraining or changing model outputs. BITCOS reduced storage to 1.485 bits per weight and increased decoding throughput by up to 18% on CPUs and 27% on GPUs. The smaller format speeds token decoding on many Intel platforms but does not help on bandwidth-rich CPUs like Lunar Lake where a fixed 2-bit kernel stays faster.
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15 October 2026
The biggest story today isn’t a single model release—it’s the plumbing getting thicker, especially for agentic AI. Nebius says it has struck deals with early-stage UK AI labs as it expands its GPU data-centre and compute offering in the UK, building on a planned £1.7bn build-out and an expanding London hub. That push rhymes with Britain’s British Business Bank, which announced £180m across six AI and deeptech VC funds, including £55m into Intrepid Growth Partners’ AI-focused Fund I, and with Nvidia’s own UK signal: CEO Jensen Huang announced minority-stake plans in five startups—Boltz, Foresight, Hiverge, Prima Mente, and Riverlane.
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