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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

vLLM is a high-performance library designed to facilitate efficient inference and serving of Large Language Models (LLMs). Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry. It offers state-of-the-art serving throughput by efficiently managing attention key and value memory through its PagedAttention mechanism. It supports continuous batching of incoming requests and utilizes optimized CUDA kernels, including integration with FlashAttention and FlashInfer, to enhance model execution speed. Additionally, vLLM provides quantization support for GPTQ, AWQ, INT4, INT8, and FP8, as well as speculative decoding capabilities. Users benefit from seamless integration with popular Hugging Face models, support for various decoding algorithms such as parallel sampling and beam search, and compatibility with NVIDIA GPUs, AMD CPUs and GPUs, Intel CPUs, and more.

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

AI infrastructure engineers looking for a solution to optimize the deployment and serving of large-scale language models in production environments

Support

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

Support

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

API

Offers API Not Supported

API

Offers API 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 Not 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

vLLM
United States
vllm.ai

Alternatives

Alternatives

DeepInfer

DeepInfer

DeepSeek
Inkling

Inkling

Thinking Machines Lab

Categories

Categories

AI Inference Supported

Integrations

Hugging Face Supported
Baseten Supported
Database Mart Not Supported
Docker Not Supported
DocsGPT Not Supported
Groq Supported
KServe Not Supported
Kubernetes Not Supported
Muse Spark Open Not Supported
NGINX Not Supported
NVIDIA DRIVE Not Supported
Nebius Supported
Nemotron 3.5 Super VL Not Supported
OpenAI Not Supported
OpenRouter Supported
PyTorch Not Supported
Recursive Flint Not Supported
Thunder Compute Not Supported
omp Not Supported
vLLM Supported

Integrations

Hugging Face Supported
Baseten Not Supported
Database Mart Supported
Docker Supported
DocsGPT Supported
Groq Not Supported
KServe Supported
Kubernetes Supported
Muse Spark Open Supported
NGINX Supported
NVIDIA DRIVE Supported
Nebius Not Supported
Nemotron 3.5 Super VL Supported
OpenAI Supported
OpenRouter Not Supported
PyTorch Supported
Recursive Flint Supported
Thunder Compute Supported
omp Supported
vLLM Not Supported
Claim Muse Spark Open and update features and information
Claim Muse Spark Open and update features and information
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