Scale Agentic Coding Workflows with Meta’s Muse Spark Open on NVIDIA GB300 NVL72

Meta launches Muse Spark Open, a coding-optimized 816B mixture-of-experts (MoE) model with 42B active parameters and a 1M token context window. Trained on NVIDIA GB300 NVL72 Blackwell Ultra specifically for agentic coding workflows, it delivers higher first attempt accuracy, reliable tool calling, and deep codebase understanding across repositories. The 1M token context window holds an entire project, including complex dependencies, legacy code, and thousands of files without breaking a task into pieces.

As a reasoning model, Muse Spark Open adds audio and video perception alongside image and document understanding, working natively and reliably across what it sees, hears, and reads. NVFP4 checkpoints generated by Meta and optimized for NVIDIA Blackwell Ultra shrink the memory footprint of all 816B parameters with native FP4 Tensor Core acceleration. Fewer bytes moved per token to increase tokens per second per user, and the freed-up memory supports larger KV cache for concurrent long context sessions, delivering higher throughput, a lower cost per token.

Custom-built for agentic coding multimodal understanding across text, image, video, and audio

General coding models are optimized for single-shot completions such as generating a function, writing a unit test, or suggesting an inline fix. They excel at discrete, well-scoped tasks where the context is small, and the answer is immediate. Agentic coding demands something fundamentally different. Planning a multifile refactor, navigating thousands of files, recovering from failing tests, and coordinating subagents over long horizons requires a model built for sustained, multistep work.