Meta launches Muse Spark Open, may appeal to enterprises

Meta launched Muse Spark Open, which it describes as the "most capable US open-weight model to date." Although Meta has been ramping its frontier model efforts, the open model from the company is arguably more important to enterprises.

Enterprises can leverage open models and customize them with their own proprietary data. Muse Spark Open is a multimodal model with 816B total parameters with 42B active and a 1M-token context window.

For many companies, Nvidia Nemotron has been the most valuable open model. Google has Gemma and Mistral Large 4 is now an option. OpenAI has gpt-oss-120b.

According to Meta, Muse Spark Open is built for coding and agentic work and long-horizon tasks, software engineering and multi-step reasoning. Meta added that Muse Spark Open is the first model that can run on your desktop using two Apple M5 Ultra Mac Studios. That move makes Muse Spark Open a play for companies looking to avoid token costs.

The release of Muse Spark Open follows the launch of Muse Wonder and Muse Wonder Full, its most advanced models yet for personal and professional agentic AI tasks. Muse Wonder is available for developers and Muse Wonder Full is available for Muse Code subscribers. Muse Wonder will be the default model in Muse shortly.

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Muse Spark Open is available via APIs on Baseten, Fireworks AI, Groq, Modal, Nebius, OpenRouter and Together AI. Model weights will be available to download on Amazon SageMaker AI, Google Cloud Model Garden, Hugging Face and Microsoft Foundry Models. Combined with Muse Spark Open's ability to run on leading inference engines and AMD, Apple, Intel and Nvidia there are multiple configurations and options for developers.

Key points:

• Muse Spark Open is optimized to run on Mac Studio with M5 Ultra.
• Two Mac Studios can become one supercomputer with a terabyte of unified memory.
• Meta said Muse Spark Open has additional safety post training.
• The model is released under the Muse Model License with limited commercial terms.

Here are the benchmarks.