Early October 15 (US Pacific), Meta released the weights of Muse Spark Open on its official blog, billing it as "the most capable US open-weight model to date." The claim carries obvious marketing flavor, but the spec sheet and engineering details show two genuinely unusual angles: an 816B-scale model that seriously considers "running on your own desk," and a deep inference-side collaboration between Meta and Apple.
816B Total Parameters, 42B Active, 1M Context
Muse Spark Open has 816B total parameters, 42B active, and a 1M-token context window — a textbook ultra-large sparse MoE layout. Meta stresses it is natively multimodal, built for complex coding tasks and agentic workloads, with strengths in long-horizon tasks, complex software engineering, and multi-step reasoning.
The ecosystem rollout is unusually complete: hosted inference is live on Baseten, Fireworks AI, Groq, Modal, Nebius, OpenRouter, and Together AI; weights are downloadable from Amazon SageMaker AI, Google Cloud Model Garden, Hugging Face, and Microsoft Foundry Models; inference engines including SGLang, vLLM, TensorRT-LLM, llama.cpp, and Apple MLX already support it, with optimizations for AMD, Apple, Intel, and NVIDIA silicon.
Two Mac Studios Stitched Into a Desktop Supercomputer
The most interesting part is the Apple silicon adaptation. Using the latest MLX framework, Muse Spark Open runs locally on two Mac Studios equipped with the M5 Ultra chip: Apple unified memory architecture lets each machine's integrated GPU address up to 512GB, so two units combine into 1TB of unified memory that holds the entire model, while each chip's 1.2TB/s of memory bandwidth is fast enough to read the small slice of weights each token needs. In Meta's phrasing, two Mac Studios behave like one supercomputer with a terabyte of unified memory.
The bar is still high: two maxed-out Mac Studios cost far beyond most individual developers' budgets, and TechCrunch notes you need at least 1TB of RAM to run it locally. It reads more as a feasibility statement — a model at this scale putting "runs at home" into its design goals for the first time.
Agentic Field Tests: Fixing an iOS App, Editing Video
Meta drove the model through fully on-device agentic workflows using Muse Code, its AI coding terminal: it shipped real iOS changes to Ice Cubes, an open-source Mastodon client — bug fixes, new features, and a full build — and operated DaVinci Resolve end to end, reviewing footage frame by frame to find the strongest moments and crop them vertically.
Ecosystem Position and a Cold Look
The open-source bundle also ships Helion GPU kernels: 1.03-3.95x kernel-level speedups on NVIDIA and AMD GPUs inside SGLang, plus a 3% end-to-end speedup on AMD MI350. The license is the Muse Model License with limited commercial terms that let most developers use it for free. The model received additional safety post-training and was evaluated under Meta's Superintelligence Scaling Framework, meeting the release thresholds.
Personal superintelligence is the vision Meta keeps repeating — the freedom to run advanced models should belong to everyone, and an 816B model on the Mac is the most concrete step toward that vision so far. A cold look, though: 42B active parameters still means nontrivial inference costs, and the dual Mac Studio setup is closer to a lab bench for enthusiasts and research teams than a mass-market product. The real question to watch: as unified memory keeps growing and the MLX ecosystem matures, will running flagship open-weight models locally shift from marketing narrative to the default option?