Meta 发布开源权重模型 Muse Spark Open，816B 参数、1M 上下文

Today, Meta is releasing the weights for Muse Spark Open, the most capable US open-weights model to date. It’s available today on Hugging Face under the Muse Model License. Muse Spark Open runs optimized on the SGLang and vLLM inference engines, with optimization for NVIDIA, AMD, and Intel.

Building on our August release of Muse Spark, this open weights version has additional safety post-training to make it more robust against misuse as a standalone model without system guardrails. The result is a model that is a top open source choice for safety. Muse Spark Open is an 816B total/42B active parameter model with a context window of 1M tokens. Built for coding and agentic work, Muse Spark Open is natively multimodal and shares the same base model as Muse Spark 1.3.

Meta loves open source, and as the creators of PyTorch, we’re committed to giving back to ensure the entire ecosystem fully supports Muse Spark Open. First, we’ve integrated Helion, our exciting new PyTorch-native high-level DSL for performant and portable ML kernels, to ship high-performance operations. Additionally, TorchTitan already supports Muse Spark out of the box for supervised fine-tuning and RL in the open, and we’re excited to see how far the PyTorch community can take it!

Muse Spark Open

Muse Spark Open is a coding-optimized open-weight model. Trained specifically for agentic coding workflows, it delivers higher first-attempt accuracy, reliable tool calling, and deep codebase understanding across the repos developers actually work in. You can read more in our previous model release blog here.

Enhanced Coding Capabilities

Strong at code generation, debugging, and codebase understanding. Trained on real developer workflows — multi-file refactors, debugging sessions, and long-running tasks — so the model keeps up with how you actually work. 1M context window means it handles your entire project including complex dependencies, legacy code, and thousands of files without needing to break your task into pieces

Multimodal Understanding

Muse Spark Open has audio and audio-video perception — alongside image and document understanding. As a reasoning model, it processes image, audio, and text inputs — natively and reliably.

Agentic

Muse Spark Open is built from the ground up as an agentic model. It maps out steps, acts, observes, and self-corrects over long horizons — not just single-shot tool calls. It’s trained to browse the web (including multimodal browsing), and use an agentic harness to: spawn and coordinate subagents for decomposable work; and leverage computer use capabilities unified across desktop, mobile, and browser.  It’s trained on long-horizon, multi-site tasks, and generates separate commentary that lets the model state intent before acting—so you can always know what it’s doing and why.

Why Helion kernels?

Helion is a PyTorch native kernel authoring language designed for writing high-performance kernels across heterogeneous hardware platforms. Following early adoption from the open-source ecosystem, Helion became a part of the PyTorch Foundation in April 2026. Muse Spark Open ships with Helion implementations of operations such as RoPE, RMSNorm, quantization, etc., delivering 1.03 – 3.95x speedups compared to custom kernels written in hardware specific languages (e.g. NVIDIA CUDA and CuTe DSL) on NVIDIA GB300. These same kernels were directly portable for AMD, scoring similar performance improvements on AMD Mi350 with an overall 3% improvement in end-to-end throughput.

In addition, we also launched Helion megakernels for Muse Spark Open in SGLang. Written entirely in Helion, these kernels fuse multiple operations into a single persistent kernel to improve low latency inference, and can likewise be autotuned for different shapes and hardware.

Helion kernels can be further autotuned by users for broader hardware platforms and workloads – including different accelerator architectures (NVIDIA, AMD, Google TPU, etc.), quantization formats, tensor shapes, and parallelism strategies – without maintaining separate hand-tuned implementations for every configuration. Together, these capabilities make it easier to achieve portable performance while reducing the cost of developing and maintaining specialized kernels.

Post-training  with TorchTitan

TorchTitan is a PyTorch native platform designed for rapid experimentation and large-scale training of generative AI models. Starting today, TorchTitan adds a model definition for Meta’s Muse Spark Open , so developers can tune it to their needs. 

TorchTitan features native support for multi-turn SFT and LoRA out-of-the-box, as well as reinforcement learning capabilities (TitanRL) that allow users to fine-tune Muse Spark Open.

Get Started

We’re sharing this work because we want everyone to have access to the best AI models. Muse Spark Open ships with custom, auto-tunable Helion GPU kernels in SGLang, delivering 1.03–3.95× kernel-level speedups on NVIDIA and AMD GPUs and a 3% end-to-end speedup on AMD MI350. You can also customize it for your use case by leveraging PyTorch’s TorchTitan to further tune the model.

Start Building Today: https://huggingface.co/meta-models/Muse-Spark-open-816B-A42B

Muse Spark is the full size model from our model family. We previously released Muse Glimmer, a small dense model. Start using it today, with ExecuTorch.

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