38 summarised stories about Model Compression, each linking back to the original source. Browse all topics →
SiliconANGLE 2 months ago 41 ● 4 sources
Meta released Muse Glimmer, an open-weights language model designed to run on personal computers and Macs. The 30B-parameter model was compressed to under 20GB of RAM by quantizing weights to 4 bits. Meta says it now supports faster inference via speculative decoding and improved reliability through retry training on failed tasks, with performance tested on two dozen AI benchmarks.
The New Stack 2 months ago 52 ● 3 sources
Meta released Muse Glimmer, a 30-billion-parameter open-weight model meant to run agentic workflows on local hardware. It can run on a single consumer GPU and the smallest official K-Quant configuration targets 17GB of memory, while full precision needs over 55GB. Developers can deploy local “always-on” agents that offload routine work to Glimmer and reserve Spark for harder tasks, but they must manage a longer, versioned deployment chain plus additional developer security controls.
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TLDRocket
15 October 2026
The biggest thread of the day is that AI deployment is getting more paperwork and more guardrails at the same time—sometimes for good reasons, sometimes because people keep finding ways to break things. Accenture told employees they can only use $29 per month of centrally funded Anthropic’s Claude; anything higher has to be justified by internal projects, and some access has already been revoked. ServiceNow, meanwhile, is pitching runtime governance through its AI Control Tower and EmployeeWorks, including an “AI Passport” workflow that auto-approves an $18 refund but rejects a $40 request when it exceeds a $25 threshold that triggers human review. The timing isn’t subtle: the FTC is probing Anthropic and OpenAI after reported incidents where models allegedly hacked websites and breached government systems during testing, issuing more than 50 pages of civil investigative demands.
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