Muse Spark Open

Muse Spark Open

Meta
5.0 out of 5 stars
+
+

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About

Muse Spark Open is an open-weight, natively multimodal language model for developers and organizations building AI applications, coding systems, and agentic workflows. The released model has 816 billion total parameters, 42 billion active parameters, and a 1-million-token context window. Teams can download the model weights, run inference through supported engines such as vLLM, SGLang, TensorRT-LLM, Hugging Face Transformers, Apple MLX, llama.cpp, or Unsloth, and customize the model using PyTorch. It supports long-context reasoning, software engineering, coding, tool use, and multimodal inputs. Deployment options include cloud inference through listed API providers or self-hosting on compatible GPU systems; Meta documents a desktop setup using two Apple M5 Ultra Mac Studio computers. The model is released under the Muse Model License with limited commercial terms.

About

Transition seamlessly between eager and graph modes with TorchScript, and accelerate the path to production with TorchServe. Scalable distributed training and performance optimization in research and production is enabled by the torch-distributed backend. A rich ecosystem of tools and libraries extends PyTorch and supports development in computer vision, NLP and more. PyTorch is well supported on major cloud platforms, providing frictionless development and easy scaling. Select your preferences and run the install command. Stable represents the most currently tested and supported version of PyTorch. This should be suitable for many users. Preview is available if you want the latest, not fully tested and supported, 1.10 builds that are generated nightly. Please ensure that you have met the prerequisites (e.g., numpy), depending on your package manager. Anaconda is our recommended package manager since it installs all dependencies.

Platforms Supported

Windows Supported
Mac Supported
Linux Supported
Cloud Supported
On-Premises Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

Platforms Supported

Windows Supported
Mac Supported
Linux Supported
Cloud Not Supported
On-Premises Not Supported
iPhone Supported
iPad Supported
Android Supported
Chromebook Not Supported

Audience

AI developers, machine learning engineers, software engineers, AI researchers, enterprise development teams, and organizations seeking customizable open-weight multimodal models for local inference, autonomous agents, complex coding, and long-context AI applications

Audience

Researchers in need of an open source machine learning solution to accelerate research prototyping and production deployment

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Not Supported

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Supported

API

Offers API Not Supported

API

Offers API Not Supported

Screenshots and Videos

Screenshots and Videos

Pricing

$0
Model weights are available at no charge under the Muse Model License, which has limited commercial-use terms. Hosted inference providers set their own usage prices.
Free Version Supported
Free Trial Not Supported

Pricing

No information available.
Free Version Not Supported
Free Trial Not Supported

Reviews/Ratings

Overall 5.0 / 5
ease 5.0 / 5

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Pros & Cons from Real Users

Pros

  • Meta finally opened up a genuinely frontier-level Muse model. The 1M-token context window and 42B active parameters make it strong enough for large repos, long agent sessions, multimodal work, and complex coding without feeling like a stripped-down open model. The local deployment story is also a huge deal. Being able to run something this capable on two M5 Ultra Mac Studios is impressive, especially for teams that want more control over data, infrastructure, and customization. I also like how much of the stack is open around it. Muse Spark Open works with MLX, vLLM, SGLang, TensorRT-LLM, Transformers, llama.cpp, and Unsloth, and Meta is making the weights available through Hugging Face, SageMaker, Google Cloud Model Garden, and Microsoft Foundry.

Cons

  • Even though it is technically desktop-capable, needing two M5 Ultra Mac Studios for the full local experience is still a serious setup, so most developers will probably use hosted inference instead.

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported

Training

Documentation Supported
Webinars Supported
Live Online Not Supported
In Person Not Supported

Company Information

Meta
Founded: 2004
United States
research.meta.ai/blog/muse-spark-open

Company Information

PyTorch
Founded: 2016
pytorch.org

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Categories

Categories

AI Development Supported
Machine Learning Supported
Neural Network Supported

Integrations

Groq Supported
vLLM Supported
Amazon EC2 G5 Instances Not Supported
Amazon EC2 Inf1 Instances Not Supported
Amazon SageMaker Debugger Not Supported
Bayesforge Not Supported
Collimator Not Supported
Cyfuture Cloud Not Supported
DagsHub Not Supported
Guild AI Not Supported
IBM Distributed AI APIs Not Supported
Intel Open Edge Platform Not Supported
Intel Tiber AI Cloud Not Supported
Keepsake Not Supported
Label Studio Not Supported
MegaETH Not Supported
NVIDIA AI Foundations Not Supported
NVIDIA PhysicsNeMo Not Supported
Thunder Compute Not Supported

Integrations

Groq Supported
vLLM Supported
Amazon EC2 G5 Instances Supported
Amazon EC2 Inf1 Instances Supported
Amazon SageMaker Debugger Supported
Bayesforge Supported
Collimator Supported
Cyfuture Cloud Supported
DagsHub Supported
Guild AI Supported
IBM Distributed AI APIs Supported
Intel Open Edge Platform Supported
Intel Tiber AI Cloud Supported
Keepsake Supported
Label Studio Supported
MegaETH Supported
NVIDIA AI Foundations Supported
NVIDIA PhysicsNeMo Supported
Thunder Compute Supported
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