Can You Run Meta Muse Spark Open Locally? Hardware, Quants and Setup

Meta is back in the open weights game. On October 15, 2026, it released Muse Spark Open, an 816B parameter mixture-of-experts model with a 1 million token context window, built for coding and agentic work, and Meta calls it the most capable open weights model from the United States. Within hours, r/LocalLLaMA was asking the only question that matters to local AI fans: can you actually run Muse Spark Open locally? The short answer is yes, but not on a gaming PC. This guide covers the real memory requirements for every quant, the hardware that works, the llama.cpp and Unsloth commands, the recommended settings, how it scores against Kimi K3, GLM 5.3 and Qwen 3.8, and what the license allows.

What Is Muse Spark Open?

Muse Spark Open is Meta’s first major open weights release since the Llama series, and it comes from the same Muse family as Meta’s paid models. The paid Muse Wonder model sits at number two on Arena Text, and Muse Wonder Full is limited to Muse Code subscribers. Muse Spark Open is the free, downloadable sibling. The Decoder notes it falls well short of the paid Muse Wonder Full models, which is expected.

Meta built it for long-horizon coding and agent tasks. According to Unsloth’s documentation, Meta trained it on work like whole-repository generation and auto-research, and Meta’s own demos paired it with Muse Code, its AI coding terminal, to fix bugs and add features in a Mastodon client, and with DaVinci Resolve to review and crop video footage.

It is also natively multimodal on the input side: you can give it text, images, video and audio, and it answers in text. That makes it one of the very few open models of this size that can look at a screenshot of a broken UI and the code behind it in the same prompt.

Muse Spark Open Specs at a Glance

The MoE design is why local inference is possible at all. Only around 42B parameters are active per token, so once the whole model fits in memory, generation speed behaves more like a 40B dense model than an 816B one. The catch is the “once it fits in memory” part.

How Much Memory Do You Need?

Unsloth published GGUF quants from dynamic 1-bit up to 8-bit, with these memory requirements. The figures are total RAM plus VRAM, or unified memory on a Mac:

Unsloth says the 2-bit UD-IQ2_XXS quant fits on 256 GB of RAM, VRAM or unified memory, and 8-bit needs about 900 GB. Two practical rules from their docs:

• Your total memory should be larger than the quant’s file size. If it is not, llama.cpp can offload to RAM or even disk, but generation slows down dramatically.
• Longer context needs more memory on top of the weights. The full 1M context is a lot of KV cache; start at 32K or 64K and increase only if you need it.

To be blunt: a single RTX 5090 with 32 GB of VRAM and 64 GB of system RAM is nowhere close. Even a 1-bit quant needs about three times that.

Hardware That Can Actually Run It

Apple Mac Studio (M5 Ultra)

Meta specifically optimised Muse Spark Open for Apple’s M5 Ultra and says it runs locally on two Mac Studios with 512 GB of unified memory each. TechCrunch reported, citing Engadget, that you need at least 1 TB of RAM for that setup. That lines up with the 8-bit numbers above. A single 512 GB Mac Studio can run the 3-bit and tight 4-bit quants, and a 256 GB model can run the smallest 2-bit quant.

CPU server with lots of RAM

A dual-socket server with 512 GB to 1 TB of DDR5 is the cheapest per gigabyte. Because only about 42B parameters are active, CPU inference is usable for experiments, but expect it to be slow next to a Mac Studio or GPUs, and prompt processing on long contexts will hurt.

Hybrid GPU plus RAM

The popular r/LocalLLaMA setup for giant MoE models is a few big GPUs plus a lot of system RAM, with llama.cpp keeping attention and shared layers on the GPU and pushing expert weights to RAM. It works, but you still need the total memory from the table above.

Datacenter GPUs

For full quality and real speed, it is a multi-GPU node job. At this point most people are better served by an API provider, which TechCrunch says is available “through a number of different API platforms”.

Which Quant Should You Pick?

Unsloth’s KL-divergence testing gives clear guidance:

• UD-Q4_K_XL has very small divergence from the full model. If you have the memory, this is the quality pick.
• Q3_K_XL is described as reasonable.
• Below UD-IQ2_S, general task quality degrades. Unsloth recommends UD-IQ2_S as the minimum for agentic and coding tasks.
• Chat-style use works fine even on the smallest quants.

So, if your goal is to use it as a coding agent, aim for UD-IQ2_S or better. If you just want to chat with it and see what Meta built, the smallest quant that fits is fine.

Run Muse Spark Open With llama.cpp

These steps follow Unsloth’s guide. At launch, building llama.cpp from main needed PR #30616 to be merged; if your build does not recognise the model, use Unsloth’s prebuilt fork at github.com/unslothai/llama.cpp.

1. Build llama.cpp

apt-get update
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
git clone https://github.com/ggml-org/llama.cpp
cmake llama.cpp -B llama.cpp/build 
    -DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=ON
cmake --build llama.cpp/build --config Release -j --clean-first 
    --target llama-cli llama-mtmd-cli llama-server llama-gguf-split
cp llama.cpp/build/bin/llama-* llama.cpp

Use -DGGML_CUDA=OFF for CPU-only builds or on Apple Silicon, where Metal is used.

2. Download a quant

pip install -U "huggingface_hub[cli]"
hf download unsloth/Muse-Spark-Open-816B-A42B-GGUF 
    --local-dir unsloth/Muse-Spark-Open-816B-A42B-GGUF 
    --include "*UD-IQ2_XXS*"

Swap the include pattern for the quant you want, for example "*UD-IQ2_S*" for agentic work or "*UD-Q4_K_XL*" if you have 512 GB or more. Make sure the drive has room: the files are split into several parts.

3. Run it

./llama.cpp/llama-cli 
    --model unsloth/Muse-Spark-Open-816B-A42B-GGUF/UD-IQ2_XXS/Muse-Spark-Open-816B-A42B-UD-IQ2_XXS-00001-of-00006.gguf 
    --temp 1.0 
    --top-p 1.0 
    --ctx-size 32768

Point --model at the first shard and llama.cpp loads the rest. Note that Unsloth’s example command uses --top-k 64, while Meta’s recommended setting is top_k disabled; both are reasonable, so try Meta’s setting first. For an OpenAI-compatible endpoint that your coding tools can talk to, use llama-server with the same model path.

Run It With Unsloth Desktop or as an API

If you would rather not compile anything, Unsloth Desktop (macOS, Windows and Linux) can search, download and run the GGUF directly, with tool calling, web search and code execution built in. Install options from Unsloth’s docs:

• macOS, Linux or WSL: curl -fsSL https://unsloth.ai/install.sh | sh
• Windows PowerShell: irm https://unsloth.ai/install.ps1 | iex
• Homebrew: brew install --cask unsloth
• WinGet: winget install --id UnslothAI.Unsloth -e
• Docker: the unsloth/unsloth image

To serve it as a local API:

unsloth run --model unsloth/Muse-Spark-Open-816B-A42B-GGUF:UD-IQ2_XXS

Review any install script before you pipe it into a shell, as with any project.

Recommended Settings

Benchmarks: Muse Spark Open vs Kimi K3, GLM 5.3 and Qwen

Meta’s official benchmark table (Muse Spark Open at xhigh reasoning) shows a model that leads on agentic and knowledge tests, and trails the best Chinese open models on pure coding benchmarks. These are Meta’s numbers, as reproduced in Unsloth’s documentation:

Two takeaways. First, Meta’s “most capable US open weights model” claim is about the US: on several coding and computer-use benchmarks, Kimi K3 and GLM 5.3 are still ahead. Second, Muse Spark Open’s standout numbers are in tool use (MCP-Atlas), multimodal reasoning and safety. On StrongREJECT v2, where lower is better, it scored 0.5 against 62.9 to 86.4 for the other models in Meta’s table, which suggests it is much harder to jailbreak.

The Decoder summarised it as “roughly on par” with the top open weights Chinese models, which is a fair reading of the table.

The License: What You Can and Cannot Do

Based on The Decoder and ThursdAI’s coverage of Meta’s license:

• The model is free to use, including for building products.
• Products must display “Built with Muse Spark Open”.
• Companies earning more than $20 million a year that want to offer the model as an API service need a separate agreement with Meta.

It is not Apache 2.0 or MIT, so read the license file on Hugging Face before you ship anything commercial, especially if you plan to host it for other people.

Cannot Run It? Your Alternatives

Most readers will not have 256 GB of memory lying around. Realistic options:

• Use an API. Muse Spark Open is available through several API platforms, so you can test it on your own prompts without buying hardware.
• Run a smaller open model. Recent options include Qwen3.8-27B (runs on about 24 GB of RAM in Ollama with ollama pull qwen3.8:27b), Liquid AI’s LFM2.5-24B-A2B for local agents, and NVIDIA’s Nemotron 3.5 Super VL (120B total, 12B active) if you have a workstation.
• Rent a big machine by the hour. Cloud instances with 512 GB+ of RAM or multiple large GPUs are the cheapest way to try the 4-bit quant for an afternoon.

If your interest in local AI is more about images, video and voice than chat models, these run on a normal 16 GB GPU: my guides to the best local AI video models, Voicebox for free local AI voices and FLUX 3 Image Klein in ComfyUI are good places to start.

Frequently Asked Questions

Can I run Muse Spark Open locally?

Yes, if you have enough memory. The smallest 2-bit quant fits in about 256 GB of RAM, VRAM or unified memory, 4-bit needs about 512 GB, and 8-bit about 900 GB. A normal gaming PC cannot run it.

Will it run on an RTX 5090?

Not on its own. 32 GB of VRAM is far below even the 1-bit quant’s 180 GB requirement. You would need hundreds of gigabytes of system RAM alongside it, and it would be slow.

What Mac do I need for Muse Spark Open?

Meta optimised it for the M5 Ultra and says two Mac Studios with 512 GB each can run it. A single 512 GB Mac Studio handles 3-bit and tight 4-bit quants; a 256 GB one handles the smallest 2-bit quant.

Is Muse Spark Open on Ollama?

The official route at launch is llama.cpp or Unsloth with the GGUF files from Unsloth and ggml-org on Hugging Face. Check the Ollama library for a current listing before you rely on it.

Can I use Muse Spark Open commercially?

Yes, with the “Built with Muse Spark Open” label. API providers with more than $20M in annual revenue need a separate agreement with Meta.

What is the best quant for coding?

Unsloth recommends UD-IQ2_S as the minimum for agentic tasks, and UD-Q4_K_XL if you have the memory, because its output stays very close to the full model.

Is Muse Spark Open better than Kimi K3?

On Meta’s own benchmarks it beats Kimi K3 on tool use, multimodal and knowledge tests, and loses on coding and computer-use tests like Terminal-Bench 2.1 and OSWorld.

Final Thoughts

Muse Spark Open is a big moment for open AI: Meta is back, the weights are on Hugging Face, and the model is genuinely competitive with the best Chinese open models while leading on tool use and safety. But “open” is not the same as “runs on your PC”. To run Muse Spark Open locally you need at least 256 GB of memory for a 2-bit quant and around 512 GB for a quant you would want to code with. If you have that hardware, the Unsloth GGUFs and llama.cpp get you running in an afternoon. If you do not, use an API to test it and keep a smaller model for daily local work.

Sources

• TechCrunch: Meta launches an open weight AI model that runs on Macs
• The Decoder: Muse Spark Open is Meta’s answer to Qwen, GLM and Kimi
• Unsloth: Muse Spark Open run locally guide
• Hugging Face: unsloth/Muse-Spark-Open-816B-A42B-GGUF
• ThursdAI: October 2026 AI releases