# Muse Spark Open

Muse Spark Open decoding rises from a 517 tok/s baseline to 1,109 tok/s by fusing each layer's attention and MoE into a single kernel that stays resident on the GPU, overlapping weight loads, tensor-core work and cross-GPU communication. Previously SGLang launched about 1,450 kernels per decoding step, and the GPU drained and refilled between them, leaving some SMs and threads idle. The figure was measured on Blackwell GPUs at batch size one with DFlash V2 speculative decoding and has not yet been extended to larger batches.

Muse Spark Open decoding rises from a 517 tok/s baseline to 1,109 tok/s by fusing each layer's attention and MoE into a single kernel that stays resident on the GPU, overlapping weight loads, tensor-core work and cross-GPU communication. Previously SGLang launched about 1,450 kernels per decoding step, and the GPU drained and refilled between them, leaving some SMs and threads idle. The figure was measured on Blackwell GPUs at batch size one with DFlash V2 speculative decoding and has not yet been extended to larger batches.

Meta released Muse Spark Open, an open-weight multimodal mixture-of-experts model, and SGLang and Miles added inference and reinforcement-learning training backends on launch day. Its text backbone has 816B parameters with about 42B active per token, accepting text, image, video and audio with a 1M-token context. On eight B300 GPUs with NVFP4 weights, an OpenHands multi-turn agentic coding replay reaches 80.3K total tokens/s per GPU at 61 tokens/s per user at concurrency 128.

Meta released Muse Spark Open, an open-weight multimodal mixture-of-experts model, and SGLang and Miles added inference and reinforcement-learning training backends on launch day. Its text backbone has 816B parameters with about 42B active per token, accepting text, image, video and audio with a 1M-token context. On eight B300 GPUs with NVFP4 weights, an OpenHands multi-turn agentic coding replay reaches 80.3K total tokens/s per GPU at 61 tokens/s per user at concurrency 128.

Meta has released the weights for Muse Spark Open, a model built for agentic coding and long-horizon tasks, and it is now served on Modal as a Shared Endpoint with per-token billing. It offers native multimodal understanding and a 1M-token context window, covering code writing and debugging, multi-step planning, sequential tool use and failure recovery across extended workflows. In Meta's evaluations it scores 76.4 on Terminal-Bench 2.1 with the Terminus 2 agent and 60 on DeepSWE 1.1 with Meta's preferred harness at xhigh reasoning effort.

Meta has released the weights for Muse Spark Open, a model built for agentic coding and long-horizon tasks, and it is now served on Modal as a Shared Endpoint with per-token billing. It offers native multimodal understanding and a 1M-token context window, covering code writing and debugging, multi-step planning, sequential tool use and failure recovery across extended workflows. In Meta's evaluations it scores 76.4 on Terminal-Bench 2.1 with the Terminus 2 agent and 60 on DeepSWE 1.1 with Meta's preferred harness at xhigh reasoning effort.

Meta released Muse Spark Open, an open mixture-of-experts model for agentic coding with 816B total parameters, 42B active per token, and a 1M token context window that holds an entire project including complex dependencies and legacy code. General coding models are optimized for single-shot completions such as generating a function or a unit test, where context is small and the answer immediate, while multifile refactors, navigation across thousands of files, recovery from failing tests, and subagent coordination require sustained multistep work. Trained on NVIDIA GB300 NVL72, the model supports tool use, output evaluation, and iterative refinement, using goal conditioning to hold direction and context compaction to sustain progress across long sessions.

Meta released Muse Spark Open, an open mixture-of-experts model for agentic coding with 816B total parameters, 42B active per token, and a 1M token context window that holds an entire project including complex dependencies and legacy code. General coding models are optimized for single-shot completions such as generating a function or a unit test, where context is small and the answer immediate, while multifile refactors, navigation across thousands of files, recovery from failing tests, and subagent coordination require sustained multistep work. Trained on NVIDIA GB300 NVL72, the model supports tool use, output evaluation, and iterative refinement, using goal conditioning to hold direction and context compaction to sustain progress across long sessions.

Meta released the weights of Muse Spark Open, an 816B-parameter omnimodal mixture-of-experts model with 42.7B active at inference, co-trained with Muse Code for coding-agent workflows. A compressed MoE block and a hybrid global/local attention schedule cut inter-GPU traffic and long-session KV cache memory, and the model supports a 1M-token context with text, image, video, and audio input. On Meta-reported benchmarks it is competitive with frontier-lab models on agentic coding and knowledge work, though each model was tested with its own lab's coding agent rather than a shared harness.

Meta released the weights of Muse Spark Open, an 816B-parameter omnimodal mixture-of-experts model with 42.7B active at inference, co-trained with Muse Code for coding-agent workflows. A compressed MoE block and a hybrid global/local attention schedule cut inter-GPU traffic and long-session KV cache memory, and the model supports a 1M-token context with text, image, video, and audio input. On Meta-reported benchmarks it is competitive with frontier-lab models on agentic coding and knowledge work, though each model was tested with its own lab's coding agent rather than a shared harness.

Meta's open-weight reasoning model Muse Spark Open is now available on AI Gateway, aimed at coding and agentic workflows such as multi-file refactoring, debugging, and understanding large codebases. It accepts text and image input with a 1M-token context window, letting agents work with code and screenshots in the same request. Users call it as meta/muse-spark-open across the AI SDK, the OpenAI-compatible Chat Completions and Responses APIs, the Anthropic-compatible Messages API, and coding agents connected to AI Gateway, without creating separate provider accounts.

Meta's open-weight reasoning model Muse Spark Open is now available on AI Gateway, aimed at coding and agentic workflows such as multi-file refactoring, debugging, and understanding large codebases. It accepts text and image input with a 1M-token context window, letting agents work with code and screenshots in the same request. Users call it as meta/muse-spark-open across the AI SDK, the OpenAI-compatible Chat Completions and Responses APIs, the Anthropic-compatible Messages API, and coding agents connected to AI Gateway, without creating separate provider accounts.

vLLM shipped inference support for Muse Spark Open on release day, a mixture-of-experts model with 816B total parameters and 42B active per token, handling a 1M context window plus native image, video and audio input. Such models usually wait weeks for community ports; here Nvidia and AMD images arrived together with a trained DSpark speculative draft model. On TPU with speculative decoding, four v7 chips exceed 650 tokens/sec and eight exceed 960 tokens/sec.

vLLM shipped inference support for Muse Spark Open on release day, a mixture-of-experts model with 816B total parameters and 42B active per token, handling a 1M context window plus native image, video and audio input. Such models usually wait weeks for community ports; here Nvidia and AMD images arrived together with a trained DSpark speculative draft model. On TPU with speculative decoding, four v7 chips exceed 650 tokens/sec and eight exceed 960 tokens/sec.