Welcome Muse Spark Open: open-weight frontier multimodal mixture-of-experts model from Meta

Meta’s Muse Spark Open is presented as an open-weight, coding-focused multimodal mixture-of-experts model with roughly 816 billion total parameters, 42 billion active parameters per token, and a context window of up to one million tokens. It accepts text, images, audio, and video while generating text, and is designed for agentic workflows such as navigating codebases, debugging, tool calling, browser interaction, and iterative self-correction. Its architecture uses 256 experts per MoE layer, selecting eight for each token, alongside dedicated perception and audio encoders; this reduces compute per token but retains substantial memory requirements, with the BF16 checkpoint occupying about 1.5 TB. The post details inference support through Transformers, llama.cpp, SGLang, vLLM, MLX, and hosted Hugging Face endpoints, including quantized variants for NVIDIA and Apple hardware, while explaining multimodal prompting, adjustable reasoning effort, and tool-call routing. It also describes LoRA fine-tuning across 64 H100 GPUs and demonstrations involving 3D asset generation, screenshot-based browser control, and voice-driven product search.

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