Short answer: yes, you can run Muse Spark Open locally — but "two Mac Studios" is the optimistic version of the truth. The 4-bit weights need about 493GB of memory, and a single M5 Ultra Mac Studio tops out at 256GB — roughly half the model. Two machines clustered over Apple's MLX framework give you 512GB, which clears the requirement by a sliver and leaves almost nothing for context. For comfortable real-world use, several reports point to closer to 1TB of RAM — three or four Mac Studios — at $10,799 apiece. So the real question isn't can you run it. It's whether a ~$22,000-to-$43,000 desk cluster is worth it over a smaller model or an API. For most people it isn't. Here's the math, and the one case where it is.
On October 15 Meta released Muse Spark Open, calling it the most capable US open-weight model to date — a sparse mixture-of-experts of roughly 816 billion total parameters with about 40B active per token, tuned for coding and agents. The news hook everyone ran with is that it runs on your desk instead of in a data center. That's genuinely new. It's also where the fine print lives.
The memory math is the whole story#
A model's local footprint is a memory problem before it's a compute problem, and Muse Spark Open makes that vivid. At 4-bit quantization the weights come to about 493GB. That number is the gate everything else passes through.
Apple's current M5 Ultra Mac Studio maxes out at 256GB of unified memory. One machine, then, holds a little over half of Muse Spark Open. To run the whole thing you cluster machines with Apple's MLX framework, which Apple extended specifically so several Macs can collectively serve one model. Meta's stated configuration is two M5 Ultra Mac Studios — 512GB combined, which fits 493GB with roughly 19GB to spare.
19GB of headroom on a 493GB model is not headroom. It's the context window, the KV cache, and your error budget, all fighting over a rounding error.
That's why the reporting diverges. CNBC describes pairing two to four Mac Studios, and other coverage puts the practical floor nearer 1TB of RAM once you account for a usable context length and the growing KV cache of an agent that runs for more than a few turns. Meta's "two Mac Studios" is true for a cold, short-context demo. A working setup that doesn't thrash wants three or four — or the 512GB Mac Studio Apple has slated for later this year, which would let two machines do it with real room.
What it actually costs#
Put dollars on each rung:
- One 256GB Mac Studio (~$10,799): runs smaller open models well; holds half of Muse Spark Open, so it can't run it alone.
- Two, clustered (~$21,598): Meta's config. It runs, with almost no headroom — fine for experiments, tight for production.
- Three to four (~$32,000–$43,000): the comfortable build the 1TB-RAM reports describe.
For comparison, renting a single 80GB H100 or MI300X runs about $2–4 an hour — but 80GB won't hold the full 4-bit model either, so renting one GPU doesn't substitute here; it's a different weight class. The honest framing is that Muse Spark Open local is a capital purchase of control, not a cost optimization.
Open-weight, not open-source — read the license#
One precision that matters for anyone building a product: Muse Spark Open is open-weight, not OSI open-source. Meta shipped it under its own Muse Model License, which permits free use for many developers but carries its own terms — it is not Apache-2.0 or MIT. If you're folding it into something commercial, read the model card the same way you'd read any open-weight coding model's license before you depend on it. The weights are on Hugging Face and, per reporting, on Amazon SageMaker AI and Google Cloud Model Garden too. Meta also positions it below Muse Wonder, its closed model from earlier in the week — so this is the second-best thing Meta shipped, offered open.
Who should actually build this#
Strip away the novelty and the decision is the usual local-LLM one, just at a bigger number. Run the Mac cluster when you specifically need a near-frontier open-weight model you fully own:
- Data that can't leave the building. Regulated or sensitive workloads where no API, zero-retention or otherwise, is acceptable.
- Independence from API pricing and access. The hedge our Founder's Wire made this week: owning the weights is insurance against a vendor's price change or a cut-off.
- Research on the weights themselves. Fine-tuning, interpretability, distillation — things an API forecloses.
For everyone else, the better answer is the one we keep giving: pick by your hardware, not the headline. A smaller open-weight model that fits one GPU will serve most builders far better per dollar, and if you just want capability on tap, an API is cheaper than a $30,000 desk. Muse Spark Open running on a Mac is a real milestone — the frontier is genuinely moving on-prem. Just don't confuse "it runs on a desk" with "it belongs on yours."
Bottom line: Muse Spark Open needs ~493GB at 4-bit, which means two 256GB Mac Studios at an absolute minimum ($21,598) and realistically three or four ($32K–$43K) until the 512GB machines arrive. It's an open-weight hedge for teams that need control, not a budget way to run a frontier model. If that's not you, a smaller local model or an API wins on every axis that isn't sovereignty.
