BaseRT vs. Muse Spark Open Comparison


BaseRT Base Compute	Muse Spark Open Meta	+	+
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About BaseRT is a high-performance LLM inference runtime for Apple Silicon that lets developers pull models from Hugging Face, chat with them locally, or serve an OpenAI-compatible API from one CLI. Accelerated by hand-written Metal kernels, it is designed to deliver fast prefill and decode performance on M-series Macs, with published benchmarks showing up to 6.4× faster prefill than llama.cpp, 3.9× faster than MLX, and up to 1.33× faster decode. The basert CLI handles model downloading, conversion, interactive chat, serving, completion, benchmarking, inspection, and bundle signing. Its server supports chat, completions, embeddings, transcription, tool calls, continuous batching, paged KV caching, and prefix caching, while supported models can process text, vision, and audio. BaseRT uses its own .base model format with Q2–Q8 affine quantization, optional AWQ calibration, and signed bundles, and can convert GGUF, Hugging Face, and MLX checkpoints.	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.
Platforms Supported Windows Not Supported Mac Supported Linux Not Supported Cloud Not Supported On-Premises Not Supported iPhone Not Supported iPad Not Supported Android Not Supported Chromebook Not Supported	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
Audience AI infrastructure engineers and developers seeking a fast, private local model inference and an OpenAI-compatible backend for applications and coding agents	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
Support Phone Support Not Supported 24/7 Live Support Not Supported Online Supported	Support Phone Support Not Supported 24/7 Live Support Not Supported Online Not Supported
API Offers API Supported	API Offers API Not Supported
Screenshots and Videos View more images or videos	Screenshots and Videos View more images or videos
Pricing No information available. Free Version Not Supported Free Trial Not Supported	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
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	Reviews/Ratings Overall 5.0 / 5 ease 5.0 / 5 Read all reviews
	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 Base Compute Founded: 2026 Australia www.basecompute.co/getbasert	Company Information Meta Founded: 2004 United States research.meta.ai/blog/muse-spark-open
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oMLX View All	Muse Glimmer Meta View All
Categories AI Tools Supported	Categories AI Coding Models Supported AI Models Supported AI Reasoning Models Supported AI Receptionists Supported Foundation Models Supported Large Language Models Supported Multimodal Models Supported Open-Weight AI Models Supported

Integrations Hugging Face Supported Baseten Not Supported Fireworks AI Not Supported Gemma 3 Supported Gemma 4 Supported Groq Not Supported Llama 3.1 Supported Llama 3.2 Supported Mistral AI Supported Modal Not Supported Nebius Not Supported OpenAI Supported OpenRouter Not Supported Phi-3 Supported Qwen3 Supported Qwen3.5 Supported Qwen3.6 Supported Together AI Not Supported vLLM Not Supported Show More Integrations View All 11 Integrations	Integrations Hugging Face Supported Baseten Supported Fireworks AI Supported Gemma 3 Not Supported Gemma 4 Not Supported Groq Supported Llama 3.1 Not Supported Llama 3.2 Not Supported Mistral AI Not Supported Modal Supported Nebius Supported OpenAI Not Supported OpenRouter Supported Phi-3 Not Supported Qwen3 Not Supported Qwen3.5 Not Supported Qwen3.6 Not Supported Together AI Supported vLLM Supported Show More Integrations View All 9 Integrations
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