@johnternus: Congrats to the team! Amazing to run a model of Muse Spark Open's caliber and scale this efficiently, right from your d…
Summary
John Ternus congratulates the team on efficiently running Muse Spark Open, a large model, on Mac Studio, highlighting advances in on-device AI performance.
View Cached Full Text
Cached at: 10/17/26, 07:01 PM
@finkd Congrats to the team! Amazing to run a model of Muse Spark Open’s caliber and scale this efficiently, right from your desk. Mac Studio keeps pushing the boundaries of on-device AI!
Similar Articles
Muse Spark Open
Meta 发布 Muse Spark Open,宣称是美国最强开源权重模型:816B 总参数(42B 激活)、1M token 上下文、原生多模态,面向编码与智能体任务,并首次可在两台 Apple M5 Ultra Mac Studio 桌面端本地运行。
@levie: They did it. They made a Muse open weights model, which is a huge step forward for OSS in the US. This is going to furt…
Aaron Levie celebrates Meta's release of the Muse open-weights model, calling it a major step forward for open-source AI in the US that will let enterprises mix frontier and custom post-trained models, boosting token consumption across the stack. The quoted post details Muse Spark Open as the most capable US open-weight model, featuring 816B total/42B active params, 1M-token context, native multimodality, and agentic coding capabilities.
@PyTorch: Today @AIatMeta introduced Muse Glimmer, an open-weight, 30-billion-parameter model distilled from Meta’s Muse Spark fo…
Meta introduced Muse Glimmer, an open-weight 30B-parameter model distilled from Muse Spark for on-device agentic workflows, with ExecuTorch now supporting running it on NVIDIA GPUs and Apple silicon.
Introducing Muse Spark Open: the most capable US open-weight model to date
Meta 发布 Muse Spark Open,号称迄今最强大的美国开源权重模型,专为编码与智能体任务打造,拥有 816B 总参数(42B 激活)、1M 上下文窗口及文本/图像/视频/音频多模态能力,并支持多种推理引擎与微调框架。
@JoacoCampoo: This has been getting a lot of interest, so decided to write about it https://memory-bound.vercel.app/blog/muse-spark-o…
一篇技术博客深入解析 Meta 于 10 月 15 日发布的开源模型 Muse Spark Open(816B 总参数、42B 激活参数的 MoE 架构),并介绍 Daniel Gross 发起的 10 万美元 Open Kernel Challenge,邀请开发者在 H200 上优化其推理内核与 SGLang 服务性能。