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Meta and Apple Release 816B Open-Weight Muse Spark Model for Two Mac Studios; Nvidia Adds Nemotron 3.5

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Score
26

Why it matters

Meta released the weights for Muse Spark Open, an 816-billion-parameter multimodal model that Meta says can run on two Apple Mac Studio computers instead of a data center. The model uses a mixture-of-experts design with 42 billion active parameters per token and a 1-million-token context window. Apple's M5 Ultra chip gives each Mac Studio up to 512 GB of unified memory, and the two machines run a 4-bit compressed version through Apple's MLX framework. Meta says the model outperforms other popular open-weight models but trails some Chinese models on certain coding tasks. Alexandr Wang, Meta's chief of AI, called it "the most capable U.S. open-weight model to date." The Muse Model License permits free use for many developers. Weights are on Hugging Face and several cloud platforms, and Together AI, Groq and Fireworks offer hosted APIs. Apple's new CEO, John Ternus, publicly praised the model after Mark Zuckerberg thanked him on X, and the companies coordinated the release.

Several claims remain unverified. A single news roundup reports that Nvidia released Nemotron 3.5 Super VL and smaller edge variants on October 15, but no other source confirms that release, and no reporting supports the "data center soft launch" described in the same summary. All performance figures come from Meta's own evaluations, and no independent benchmark has been published. Pricing for the required Mac hardware has not been confirmed. Apple is expected to release a 512 GB memory product in late October, but its price has not been disclosed. Running the model locally still requires significant computing power.

For attorneys, the release matters because it tests whether frontier-scale models can run on desktop hardware rather than cloud clusters, which bears on cost, data privacy and on-device AI deployment. Clients evaluating open-weight models should check the Muse Model License terms, particularly for commercial use, before deployment. Watch for independent benchmarks, Apple's late-October pricing, and any confirmation of the Nvidia release, which should not be relied on until a second source reports it.

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