Can You Run Muse Spark Open Locally? Yes on One Mac Studio, Not Through Ollama

Last updated: October 2026

Yes, if you have at least 256GB of memory, and realistically 512GB. Muse Spark Open's smallest GGUF build is 176GB at 1-bit. The 2-bit build Hugging Face tested is 304GB and runs at about 18 tokens per second on a 512GB Mac Studio. A 4-bit build needs about 470GB, which fits the same Mac once you raise its GPU memory limit.

Meta released Muse Spark Open on October 15, 2026, as the open-weight version of its hosted Muse Spark model: a mixture-of-experts design with 816 billion parameters, 42 billion active per token, a 1M-token context window, and text, image, audio and video input. Meta's launch announcement calls it the most capable US open-weight model to date.

The Memory Math: Every Muse Spark Open Build by Size

Muse Spark Open's weights take 176GB at 1-bit, 226 to 311GB at 2-bit, 340 to 401GB at 3-bit and 470 to 506GB at 4-bit, before system and cache overhead.

File totals from the Unsloth and ggml-org repositories on Hugging Face (October 15, 2026), Meta's developer documentation (NVFP4) and Hugging Face's launch post (MLX). Image, audio and video input add a 2 to 4GB encoder file.

The context cache is smaller than the launch math suggests

Hugging Face's launch post puts the cache at about 250KB per token, or 266GB at the full 1M tokens, counting all 62 layers. But Meta's configuration, mirrored in Intel's INT4 repository, gives 46 of those layers a 2,048-token sliding window. By our arithmetic, that leaves about 64KB per token at 16-bit: roughly 9GB at 128K tokens and 69GB at 1M, if your runtime keeps a small rolling cache for the sliding layers.

Downloading it

At a real-world 1Gbps, the 176GB file takes about 25 minutes, the 470GB file about 70 and the 1.64TB original about four hours; a 300Mbps plan takes about three times as long. Our Kimi K3 download-time guide shows how to estimate your own line; check for a data cap before pulling the 16-bit files. A 4TB NVMe drive holds the 4-bit build with room to spare.

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Which Machines Run It, and How Fast

One 512GB Mac Studio is the simplest machine that runs Muse Spark Open well, and every published speed so far comes from Apple Silicon.

Sources: Hugging Face's launch post and Apple Machine Learning Research (8,192-token context; two Macs managed 15.7 tok/s with a simpler pipeline split; first-token waits were 8.2 to 8.4 seconds on two Macs, 4.8 on four). No GPU, PC or 256GB results had been published as of October 15, 2026.

One 512GB Mac Studio

The measured single-Mac runs used the previous M3 Ultra; the M5 Ultra Mac Studio has about 50 percent more memory bandwidth (1.2TB/s against 819GB/s), the main limit on generation speed, but its 512GB configuration ships in late October at an unannounced price. Raise macOS's GPU memory limit and either Mac holds the 470GB 4-bit file; our calculator estimates 17 tokens per second on the M3 Ultra and 26 on the M5. Our M5 Ultra Mac Studio guide covers buy-or-wait, and our Mistral Large 4 reality check maps the other model headed for this class.

Two or four Mac Studios

Meta's reference setup splits the NVFP4 checkpoint across two 512GB M5 Ultras or four 256GB units, using MLX over Thunderbolt 5 with RDMA. It needs macOS 26.2 or later, MLX 0.30.1 or later and a direct Thunderbolt link between every pair of Macs. AppleInsider (October 15) puts such a cluster at tens of thousands of dollars.

256GB machines: up to 2-bit

A 256GB Mac Studio holds the 176GB 1-bit file at macOS's default GPU memory limit and the 226GB 2-bit file once you raise it; two 128GB DGX Sparks could share the 1-bit file through llama.cpp's RPC mode. Our calculator estimates 21 tokens per second at 2-bit on an M3 Ultra and 33 on an M5 Ultra. Below 3-bit, expect real quality loss on a model this size.

Workstations and servers

The mixture-of-experts design is what makes a PC workable. Every layer routes each token to 8 of 256 experts, and the experts hold about 97 percent of the weights, so llama.cpp can keep attention and part of the experts on the graphics card and the rest in system RAM, with --n-cpu-moe setting the split. A server with 768GB or more of multi-channel DDR5 can run the 470GB Q4_K_S file this way, and four 96GB workstation cards hold the 340GB 3-bit file entirely in GPU memory. Neither had a published speed at launch; our local AI hardware guide covers the build trade-offs.

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Too small: 128GB machines (DGX Spark, Ryzen AI Max+ 395 mini PCs, 128GB Macs) cannot hold even the 1-bit file; a PC needs about 192GB of RAM behind its graphics card.

The Ollama Catch: muse-spark-open Runs in the Cloud

Ollama's official library lists one tag for Muse Spark Open, muse-spark-open:cloud, as of October 15, 2026, so ollama run muse-spark-open sends your prompts to a hosted server instead of running on your machine.

The cloud tag is billed per token, like an API: a quick way to try the model, but your prompts and code are processed on Ollama's servers. For local use:

• llama.cpp: update to the newest build, then run llama serve -hf ggml-org/Muse-Spark-Open-816B-A42B-GGUF:Q2_K, the command Hugging Face and Meta publish. Swap in unsloth/Muse-Spark-Open-816B-A42B-GGUF:UD-IQ1_S for a 256GB machine.
• MLX on a Mac: mlx-community has 3-bit and 4-bit conversions; Hugging Face says mlx-lm support comes through a pull request, so you may need mlx-lm from source until a release includes it.
• LM Studio: it uses its bundled llama.cpp runtime; we had not confirmed a catalog listing at launch.

Meta's download page wants your identity

Meta's own Hugging Face repository is gated: the access form asks for your name, date of birth, country, affiliation and job title, records your location from your IP address, and falls under Meta's privacy policy. The GGUF repositories from ggml-org (the llama.cpp project) and Unsloth were not gated when we checked on October 15, though the license still applies to those copies. Stick to those three publishers rather than unknown uploaders.

Is It Worth Running? What Meta's Benchmarks Show

On Meta's own numbers, Muse Spark Open is a specialist: it leads on tool use and factual reliability, trails the strongest open models on coding agents and overall score, and refuses more often than its peers.

All figures from Meta's model card, run at Meta's highest (xhigh) reasoning setting. Artificial Analysis had not published its own score by October 15, 2026, and Meta's full evaluation report is still to come.

Score per gigabyte

GLM-5.3 scores 45 from a 467GB 4-bit file, smaller than Muse Spark Open's 506GB file of the same Unsloth type; our GLM-5.3 coverage has details. GLM-5.3-Flash scores 42 from 200GB and Qwen3.8-Flash-Next 40 from 111GB, per Artificial Analysis scores reported by VentureBeat.

The refusals

Muse Spark Open is the hardest model in Meta's comparison to jailbreak: on StrongREJECT, where lower means fewer successful jailbreaks, it scores 0.5 against 62.9 to 66.6 for GLM-5.3, Qwen3.8-2.4T and Kimi K3. The cost shows on Scale AI's FORTRESS test, which also sends benign versions of risky prompts. Its over-refusal score is 17.4, the highest in the table (Qwen3.8-2.4T 12.8, GLM-5.3 3.2, Kimi K3 2.7), so expect more refusals on legitimate questions near security, weapons or chemistry. The usage policy also forbids intentionally circumventing or removing safety measures, so tuning those refusals away breaks the license. You own the copy, not the right to strip its safeguards.

The License: Open Weights With Conditions

The Muse Model License lets you run, modify, fine-tune, distill and sell products built on Muse Spark Open, but it is a custom license, not an open-source one.

• Attribution: products must display "Built with Muse Spark Open" in the interface, documentation or settings.
• Hosting businesses: a model-as-a-service business above $20 million in gross revenue (with affiliates) in any 12 months must notify Meta and sign a separate agreement; internal use, research and features inside your own product are exempt.
• Derivatives: any model created or trained using Muse Spark Open or its outputs counts as a derivative under the license.
• Safety: the usage policy prohibits circumventing or removing safety measures, alongside the usual bans on malware, weapons and fraud.

The license has no monthly-active-user cap and no European Union exclusion, unlike Llama 4's, which carried a 700-million-user clause and withheld multimodal rights from EU-based users. Meta's smaller Muse Glimmer ships under Apache 2.0 with none of these conditions.

What to Run Instead With Less Memory

With less than 256GB, skip Muse Spark Open: several open models score as high or higher on hardware you may already own.

On a 24GB graphics card or Mac, Qwen3.8-27B scores 34 on the Artificial Analysis index from a 16.5GB 4-bit file; our Qwen3.8-27B guide covers the setup. Meta's own Muse Glimmer 30B runs locally in Ollama at 18GB under Apache 2.0, but it scores 17.

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With 128GB to 256GB, Qwen3.8-Flash-Next (40) fits a 128GB machine below 4-bit and GLM-5.3-Flash (42) is our 256GB pick; our GLM-5.3-Flash reality check has sizes and speeds, and our guide to the best local AI models by VRAM and memory covers every tier.

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Not sure what your own machine can run? Our LLM VRAM calculator grades Muse Spark Open against your graphics card, Mac or mini PC and prints the file and command to use. It runs in your browser, with no sign-up.

Check your hardware for Muse Spark Open

Frequently Asked Questions

Can I run Muse Spark Open on a 256GB Mac Studio?

Yes, at 1-bit or 2-bit. Unsloth's 176GB UD-IQ1_S file fits within macOS's default GPU memory limit, and its 226GB UD-IQ2_XXS file fits once you raise that limit. No speed had been published as of October 15, 2026, and both cost real quality. Anything larger needs a 512GB Mac.

Does Ollama run Muse Spark Open locally?

Not as of October 15, 2026. Ollama's official library lists only muse-spark-open:cloud, a hosted tag billed per token, so your prompts are processed on Ollama's servers. To run it locally, use llama.cpp with a GGUF file from ggml-org or Unsloth, or MLX on a Mac.

How much memory does Muse Spark Open need at 4-bit?

About 470GB for the Q4_K_S GGUF, 506GB for Unsloth's UD-Q4_K_XL and about 537GB for Meta's NVFP4 checkpoint. Both GGUF files fit one 512GB Mac Studio with its GPU memory limit raised; NVFP4 needs two linked Macs or a four-GPU Blackwell server, and a server with 768GB or more of RAM can split a GGUF between CPU and GPU.

Is Muse Spark Open open source?

No. Its weights are downloadable under Meta's custom Muse Model License, which permits use, fine-tuning, distillation and commercial products but requires a "Built with Muse Spark Open" credit, a separate agreement for hosting businesses above $20 million a year, and a usage policy that bans removing its safety measures.

Do I have to give Meta personal information to download it?

From Meta's own Hugging Face repository, yes: the access form asks for your name, date of birth, country, affiliation and job title, and records your location from your IP address. The GGUF copies from ggml-org and Unsloth were not gated on October 15, 2026. The license terms apply wherever you download the weights.

Is Muse Spark Open better than GLM-5.3 or Kimi K3?

Not overall, by Meta's own numbers. It scores 39 on the Artificial Analysis Intelligence Index against 45 for GLM-5.3 and 44 for Kimi K3, and it trails both on coding-agent tests such as Terminal-Bench 2.1. It leads them on tool use (MCP-Atlas) and factual reliability (AA-Omniscience). Independent scores had not been published at launch.