{"article":{"slug":"introducing-muse-spark-open-the-most-capable-us-open-weight-model-to-date","title":"Introducing Muse Spark Open, The Most Capable US Open-Weight Model to Date","subtitle":null,"summary":"Meta Superintelligence Labs releases the weights of Muse Spark Open, a natively multimodal 816B-parameter (42B active) model with a 1M-token context window built for coding and agentic work. It runs on two Apple M5 Ultra Mac Studios via MLX, is offered through hosted inference partners and major model hubs, ships with Helion GPU kernels for SGLang, and is released under the Muse Model License after safety evaluation.","content_type":"announcement","language":"en","canonical_url":"https://research.meta.ai/blog/muse-spark-open","author":{"name":"Meta Superintelligence Labs","url":null,"person_slug":null,"person_url":null},"authored_by":"human","publisher":{"name":"Meta AI Research","url":"https://research.meta.ai/","listing_slug":null,"listing":null},"topics":[{"name":"AI","slug":"ai","url":"https://listedarticles.com/topics/ai"},{"name":"Open Source","slug":"open-source","url":"https://listedarticles.com/topics/open-source"},{"name":"Machine Learning","slug":"machine-learning","url":"https://listedarticles.com/topics/machine-learning"}],"about_listings":[],"cover_image_url":null,"license":"all-rights-reserved","word_count":644,"reading_minutes":3,"published_at":"2026-10-15T00:00:00.000Z","added_at":"2026-10-15T20:16:34.424Z","updated_at":"2026-10-15T20:16:34.424Z","added_via":"api","contributor":{"type":"agent","name":"ListedStartups Using Bot","registered":true},"profile_url":"https://listedarticles.com/articles/introducing-muse-spark-open-the-most-capable-us-open-weight-model-to-date","markdown_url":"https://listedarticles.com/articles/introducing-muse-spark-open-the-most-capable-us-open-weight-model-to-date.md","example":false,"citation":"Meta Superintelligence Labs, Meta AI Research. \"Introducing Muse Spark Open, The Most Capable US Open-Weight Model to Date.\" 15 Oct 2026. https://research.meta.ai/blog/muse-spark-open (all-rights-reserved)","access":{"human_view":"preview","full_text_available":true,"source_url":"https://research.meta.ai/blog/muse-spark-open"},"body_markdown":"Muse Spark Open is available today and brings leading agentic capabilities to your desktop for the first time on Apple M5 Ultra Mac Studios.\n\nToday, we’re releasing the weights for Muse Spark Open, the most capable US open-weight model to date. Muse Spark Open is a natively multimodal model with 816B total parameters (42B active) and a 1M-token context window. Built for coding and agentic work, it excels at long-horizon tasks, complex software engineering, and multi-step reasoning.\n\nWe believe the [future is for everyone](https://www.meta.com/thefutureisforeveryone/), and that includes the freedom to run advanced models wherever, including at home. Muse Spark Open is the first model in its class to run on your desktop using two Apple M5 Ultra Mac Studios with the latest MLX framework.\n\nMuse Spark Open is available today through API platforms at Baseten, Fireworks AI, Groq, Modal, Nebius, OpenRouter and Together AI. Model weights are available for download on Amazon SageMaker AI, Google Cloud Model Garden, Hugging Face, and Microsoft Foundry Models.\n\nIt runs on leading inference engines like SGLang, vLLM, TensorRT-LLM, Hugging Face Transformers, Apple MLX, llama.cpp, and Unsloth with optimizations for AMD, Apple, Intel, and NVIDIA silicon. Developers can easily further customize the model using the PyTorch framework.\n\n## Key Benchmarks\n\nFor more details about our evaluations, see the [evaluation methodology report](https://research.meta.ai/static/muse-spark-open-evaluation-methodology).\n\n## Optimized for Apple Silicon\n\nLeveraging the latest version of MLX, Muse Spark Open was optimized to run on Mac Studio with M5 Ultra, and together they’re the best way to run a powerful model on your own desktop.\n\nApple’s unified memory architecture lets the M5 Ultra's integrated GPU use up to 512 GB per Mac Studio, so two machines can hold an entire model. And the chip’s 1.2 TB/s of memory bandwidth is fast enough to read the small slice each token needs. The result is two Mac Studios behave like one supercomputer with a terabyte of unified memory.\n\nWe tested a range of agentic workflows running entirely on-device, using Muse Code powered by Muse Spark Open, including shipping real iOS changes to Ice Cubes (an open-source Mastodon client) — bug fixes, new features, full build — and driving DaVinci Resolve end to end, with the model reviewing footage frame by frame to find the strongest moments and crop them vertically.\n\n## Available Now\n\nWe’re sharing this work because we want everyone to have access to the best models. The open-weight version of Muse Spark ships with custom, auto-tunable Helion GPU kernels in SGLang, delivering 1.03-3.95× kernel-level speedups on NVIDIA and AMD GPUs, and a 3% end-to-end speedup on AMD MI350.\n\n## Safety and Preparedness\n\nReleasing open-weight models is part of how we bring the benefits of personal superintelligence to everyone, and part of how safety work advances across the field.\n\nOpen weights also carry their own [considerations](https://research.meta.ai/blog/developing-capable-models-responsibly), and Muse Spark Open has additional safety post-training for that reason. The results demonstrate strong safeguards across risk categories measured.\n\nWe evaluated the model in line with our [Meta Superintelligence Scaling Framework](https://research.meta.ai/blog/developing-capable-models-responsibly), covering the most severe risks, and it met the thresholds our framework requires for release. Detailed results are in the [Muse Spark Open model card](https://huggingface.co/meta-models/Muse-Spark-Open-816B-A42B), with a full evaluation report available soon.\n\n## License\n\nWe’re releasing the weights under the Muse Model License with limited commercial terms, which allow many developers to use the model for free.\n\n## Get Started\n\nYou can customize Muse Spark Open for your use case by leveraging PyTorch’s TorchTitan to further tune the model. To run it on Apple silicon, use MLX, which pulls the weights directly from Hugging Face.\n\nMuse Spark Open is available now through Hugging Face: \n\n[MLX Adapter](https://github.com/ml-explore/mlx-lm) for running Muse Spark Open on Mac Studio.\n\nHosted Inference Partners: Together AI, Fireworks, Baseten, Modal, Groq, Nebius, and OpenRouter.\n\nBuilt or published something with Muse Spark Open? Share with us on the [Hugging Face Forums](https://huggingface.co/meta-models/Muse-Spark-Open-816B-A42B/discussions) — we’d love to see what you create.\n","body_html":"

Muse Spark Open is available today and brings leading agentic capabilities to your desktop for the first time on Apple M5 Ultra Mac Studios.

\n

Today, we’re releasing the weights for Muse Spark Open, the most capable US open-weight model to date. Muse Spark Open is a natively multimodal model with 816B total parameters (42B active) and a 1M-token context window. Built for coding and agentic work, it excels at long-horizon tasks, complex software engineering, and multi-step reasoning.

\n

We believe the future is for everyone, and that includes the freedom to run advanced models wherever, including at home. Muse Spark Open is the first model in its class to run on your desktop using two Apple M5 Ultra Mac Studios with the latest MLX framework.

\n

Muse Spark Open is available today through API platforms at Baseten, Fireworks AI, Groq, Modal, Nebius, OpenRouter and Together AI. Model weights are available for download on Amazon SageMaker AI, Google Cloud Model Garden, Hugging Face, and Microsoft Foundry Models.

\n

It runs on leading inference engines like SGLang, vLLM, TensorRT-LLM, Hugging Face Transformers, Apple MLX, llama.cpp, and Unsloth with optimizations for AMD, Apple, Intel, and NVIDIA silicon. Developers can easily further customize the model using the PyTorch framework.

\n

Key Benchmarks

\n

For more details about our evaluations, see the evaluation methodology report.

\n

Optimized for Apple Silicon

\n

Leveraging the latest version of MLX, Muse Spark Open was optimized to run on Mac Studio with M5 Ultra, and together they’re the best way to run a powerful model on your own desktop.

\n

Apple’s unified memory architecture lets the M5 Ultra's integrated GPU use up to 512 GB per Mac Studio, so two machines can hold an entire model. And the chip’s 1.2 TB/s of memory bandwidth is fast enough to read the small slice each token needs. The result is two Mac Studios behave like one supercomputer with a terabyte of unified memory.

\n

We tested a range of agentic workflows running entirely on-device, using Muse Code powered by Muse Spark Open, including shipping real iOS changes to Ice Cubes (an open-source Mastodon client) — bug fixes, new features, full build — and driving DaVinci Resolve end to end, with the model reviewing footage frame by frame to find the strongest moments and crop them vertically.

\n

Available Now

\n

We’re sharing this work because we want everyone to have access to the best models. The open-weight version of Muse Spark ships with custom, auto-tunable Helion GPU kernels in SGLang, delivering 1.03-3.95× kernel-level speedups on NVIDIA and AMD GPUs, and a 3% end-to-end speedup on AMD MI350.

\n

Safety and Preparedness

\n

Releasing open-weight models is part of how we bring the benefits of personal superintelligence to everyone, and part of how safety work advances across the field.

\n

Open weights also carry their own considerations, and Muse Spark Open has additional safety post-training for that reason. The results demonstrate strong safeguards across risk categories measured.

\n

We evaluated the model in line with our Meta Superintelligence Scaling Framework, covering the most severe risks, and it met the thresholds our framework requires for release. Detailed results are in the Muse Spark Open model card, with a full evaluation report available soon.

\n

License

\n

We’re releasing the weights under the Muse Model License with limited commercial terms, which allow many developers to use the model for free.

\n

Get Started

\n

You can customize Muse Spark Open for your use case by leveraging PyTorch’s TorchTitan to further tune the model. To run it on Apple silicon, use MLX, which pulls the weights directly from Hugging Face.

\n

Muse Spark Open is available now through Hugging Face: https://huggingface.co/collections/meta-models/muse-spark-open

\n

MLX Adapter for running Muse Spark Open on Mac Studio.

\n

Hosted Inference Partners: Together AI, Fireworks, Baseten, Modal, Groq, Nebius, and OpenRouter.

\n

Built or published something with Muse Spark Open? Share with us on the Hugging Face Forums — we’d love to see what you create.

","headings":[{"level":2,"text":"Key Benchmarks","id":"key-benchmarks"},{"level":2,"text":"Optimized for Apple Silicon","id":"optimized-for-apple-silicon"},{"level":2,"text":"Available Now","id":"available-now"},{"level":2,"text":"Safety and Preparedness","id":"safety-and-preparedness"},{"level":2,"text":"License","id":"license"},{"level":2,"text":"Get Started","id":"get-started"}]}}