Muse Spark Open, Meta’s coding-optimized open-weight model, is now available in Amazon SageMaker JumpStart, the machine learning (ML) hub of Amazon SageMaker AI. The model was trained on real developer workflows, such as multi-file refactors and long debugging sessions, and it supports a context window of 1 million tokens. You can deploy it to a SageMaker AI endpoint in your own AWS account in a few steps, without managing the serving infrastructure yourself. SageMaker JumpStart offers two variants: BF16 (Muse-Spark-Open-816B-A42B) and NVFP4 quantized (Muse-Spark-Open-816B-A42B-NVFP4).
In this post, we introduce Muse Spark Open and show you how to deploy the model with Amazon SageMaker Studio and the SageMaker Python SDK, send it a first inference request, and clean up.
Meta describes Muse Spark Open as a coding-optimized model trained for agentic coding workflows, with a focus on first-attempt accuracy, tool calling, and understanding of large codebases. It’s a reasoning model, and it accepts text, images, documents, audio, and audio-video as input and returns text.
Muse Spark Open uses a mixture-of-experts (MoE) architecture with 816 billion total parameters, of which 42 billion are active for each token. The model holds the capacity of a very large network, while the compute it spends on each token is closer to that of a much smaller dense model. Meta releases the weights under the Muse Model License, which includes the Muse Spark Usage Policy.
Meta trained Muse Spark Open on the way developers work in real repositories: code generation, debugging, multi-file refactors, and tasks that run for a long time.
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