Meta's Muse Spark Open Model Optimized for Agentic Coding Workflows on NVIDIA GB300 NVL72

Muse Spark Open, a new coding-optimized model developed by Meta, has been optimized to run efficiently on the NVIDIA GB300 NVL72 platform. This 816B mixture-of-experts (MoE) model boasts an impressive 42 billion active parameters and a massive 1 million token context window, allowing it to hold entire projects with complex dependencies in memory without breaking tasks into smaller pieces.

The model’s architecture is specifically designed for agentic coding workflows, which require sustained multistep work. Unlike general-purpose coding models that excel at single-shot completions like generating functions or writing unit tests, Muse Spark Open can plan, act, observe, and self-correct across long-running sessions. It supports multistep task execution with tool use, output evaluation, and iterative refinement.

Muse Spark Open’s 1 million token context window enables it to process complex projects with thousands of files without breaking tasks into smaller pieces. This is particularly useful for planning multifile refactorings or navigating large codebases. The model uses goal conditioning to hold direction and context compaction to retain the knowledge needed to sustain progress across long-running sessions.

The model’s perception encoder has a massive 1.8 billion parameter count, allowing it to natively process interleaved text, images, video frames, and audio without separate pipelines per modality. This enables developers to reason over what they see, hear, and read in a single unified pass, making it ideal for workflows spanning screenshots, diagrams, error logs, and video walkthroughs.

Muse Spark Open’s sparse MoE routing activates only 42 billion of its total 816 billion parameters per token, delivering frontier agentic capability at a fraction of the serving cost of dense models at equivalent scale. This is particularly useful for large-scale production deployments where computational resources are limited.

The NVIDIA GB300 NVL72 platform features a rack-scale architecture that integrates 72 Blackwell Ultra GPUs into a single platform. Its massive 130 TB/s bandwidth enables efficient communication between expert traffic, eliminating bottlenecks that appear when traditional off-the-shelf networks are used.

Muse Spark Open’s optimized performance on the NVIDIA GB300 NVL72 has been demonstrated to deliver up to 50K tokens per second per GPU and up to 600 tokens per second per user. This enables AI factories to run large parameter models in production at high throughput and low latency, making it an attractive solution for developers looking to deploy agentic coding workflows.

Muse Spark Open can be deployed using multiple inference stacks supported by NVIDIA, including SGLang, vLLM, and TensorRT LLM. These stacks provide direct control over serving configuration, parallelism, and batching on the NVIDIA accelerated computing platform, allowing developers to fine-tune their deployments for optimal performance.

Developers can post-train Muse Spark Open using NVIDIA NeMo AutoModel, a PyTorch native training library that supports full SFT and LoRA fine tuning out of the box. This enables rapid experimentation over a wide variety of industry-specific cases without requiring model conversion or retraining from scratch.

To get started with GPU-accelerated Muse Spark Open, developers can download the NVFP4 weights from Hugging Face or try the downloadable NVIDIA NIM for production deployments. The NIM reduces setup to a single command and automatically selects the best serving backend and precision for the detected hardware.