Two Mac Studios for Muse Spark Open: The Real Cost Is $44,598

Bottom Line

• One Mac cannot hold Meta’s 4-bit file. The NVFP4 checkpoint is 533 GB (496 GiB). Apple says a single Mac Studio cannot run it. A smaller 3-bit file fits one 512GB Mac; see Can I run Muse Spark Open locally?
• The real price is $44,598. Meta’s setup is two 512GB M5 Ultra Mac Studios at $22,299 each. The famous $29,799 Mac includes a 16TB SSD that adds $7,500 and does nothing for the model.
• Apple measured 24.99 tok/s on two Macs and 36.73 tok/s on four, at an 8,192-token prompt.
• Four 256GB Macs are cheaper and faster. $43,196 with the 80-core GPU, or $37,996 with the 64-core GPU.
• Against the API, it takes 13 years to break even. At $4.25 per million output tokens, two Macs running 24/7 save about $3,349 a year.
• Buy it for privacy, ownership or shared use. Do not buy it to save money. Test the model on the API first. A few dollars tells you if it can do your work.

Not ready for $44,598? See the tested hardware list for machines that run smaller open models well.

What Muse Spark Open Is

Meta released Muse Spark Open on 15 October 2026. Meta calls it “the most capable US open-weight model to date”. The page does not name the models it compared against.

Only 42B parameters work on each token, but all 816B must sit in memory. The next token can use different experts. So the model is cheap to compute and expensive to hold.

The memory math. At 4 bits, one parameter takes half a byte. 816B x 0.5 byte = 408 GB of raw weights. Meta’s NVFP4 file is bigger because only the experts are quantized. The rest stays at higher precision.

496 GiB does not fit in 512 GB. A 512GB Mac has 512 GiB, and macOS needs some of it. The conversation cache needs more. That is why Meta recommends two Macs.

Why Two Macs, and Why Not One

Meta lists two Apple setups:

1. Two Mac Studios, M5 Ultra, 512GB each.
2. Four Mac Studios, 256GB each, 1TB in total.

The Macs connect over Thunderbolt 5 and run MLX distributed inference. Each Mac holds part of the model. Together they act like one machine with a terabyte of memory.

A smaller file is the one-Mac option. The MLX 3-bit build is 406 GiB and Unsloth’s UD-Q3_K_XL GGUF is 374 GiB. Both fit one 512GB Mac ($22,299) after you raise the GPU memory limit. The 1-bit and 2-bit GGUFs (164-210 GiB) fit one 256GB Mac ($9,499). Each step down loses quality against Meta’s 4-bit file, and we found no published speed for any of them. Our Muse Spark Open fit page has every file size and the cache math.

Elon’s $29,799 Against the Real Number

The news coverage used the most expensive configuration. Here is what that price contains:

The model needs memory, not storage. A 1TB SSD holds the 530 GB file. So the Mac you need is $22,299, and two of them cost $44,598 before tax.

The memory upgrade alone is $15,500. That is more than two base M5 Ultra Mac Studios.

These Macs are not in anyone’s hands yet. 9to5Mac reports delivery in late November to early December. Every speed number below comes from Apple’s own tests.

How Fast Is It?

Apple published MLX numbers for M5 Ultra Mac Studios. The test used an 8,192-token prompt and 128 generated tokens.

25 tok/s is fine for reading. It is faster than most people read.

Long prompts are the problem. The model supports 1M tokens, but Apple’s table stops at 8,192. At 65,536 tokens, four Macs needed about 40.6 seconds before the first word, with two Thunderbolt cables per link. With one cable it was about 50.5 seconds. A coding agent sends long prompts again and again, so it will feel much slower than 25 tok/s suggests.

Use tensor parallel, not pipeline. On two Macs, tensor parallel decodes 59% faster (24.99 against 15.74).

The Break-Even Math

Aggregators list Muse Spark Open on OpenRouter and other providers at $1.25 per million input tokens and $4.25 per million output tokens. Cached input is about $0.15 per million.

We give the Macs the best case: they run 24 hours a day, 365 days a year, and never wait for you.

*Apple did not say which GPU it tested. Both GPUs have 1.2 TB/s of bandwidth, so decode should be close. Prompt processing will be slower on the 64-core GPU.

The arithmetic: 24.99 tok/s x 31,536,000 seconds = 788.1M tokens. 788.1M x $4.25 = $3,349. $44,598 / $3,349 = 13.3 years.

Even if the API price doubles, two Macs need more than 6 years. And a better model will probably ship before then.

This table counts output only. Input tokens also cost money on the API, so the real gap is smaller. Electricity and resale value are not counted either. Our electricity break-even page has the method for your power price.

The Cheaper Setup Is Also Faster

Four 256GB Macs beat two 512GB Macs on both price and speed. The step from 256GB to 512GB costs $11,500 per Mac. Four smaller Macs give the same 1TB in total.

• Price: $43,196 with the 80-core GPU ($10,799 each). That is $1,402 less than two big Macs. With the 64-core GPU ($9,499 each) it is $37,996, which is $6,602 less.
• Speed: 36.73 tok/s against 24.99. More chips read the weights at the same time.
• First token: 4.8 s against 8.2 s at 8,192 tokens.

The cost is complexity. Four machines need more Thunderbolt 5 cables, more desk space and more things to break. If you buy, buy four. It does not change the break-even answer much.

The Case for Buying Anyway

The break-even math is not the whole story. There are four honest reasons to buy.

1. It is cheap next to a GPU server. The other way to get about a terabyte of fast AI memory is a rented server with eight B200 GPUs. When we priced one in July 2026 for a CloudYeti video, one node cost roughly $39,000 to $72,000 a month. Two Macs cost about one month of that rent, and you keep them.
2. You buy memory, not one model. The next big open model from DeepSeek, Kimi or Qwen runs on the same terabyte, if it fits.
3. Input tokens are free at home. A coding agent reads far more tokens than it writes. Our table counts output only, so for agent work the real break-even is shorter. How much shorter depends on your cache hit rate on the API and on the slow long-prompt start on the Macs.
4. Nobody can take it away. No rate limits, no price changes, and nobody retires the model you depend on. When CloudYeti asked local AI rig owners whether they would buy again, this was the most upvoted answer.

Who Should Buy This

• Your data cannot leave the building. Client records, internal code, regulated data. Then the API price does not matter. A terabyte of local memory for under $45,000 is cheap next to a GPU server.
• A small team shares it. One machine serves several people, so the cost per person drops. Expect slower answers when two requests run at once.
• You want to save money as one person. It will not. The API price is too low.

Want Meta’s model at home without $44,598? Run the smaller one. Muse Glimmer 30B is about 52 GiB at full BF16 precision, so it fits on an older Mac Studio M4 Max with 128GB. That is the previous-generation chip, not the M5 Ultra, and it cannot run Muse Spark Open. A 48GB+ Mac runs Glimmer at 4 to 6 bits. See Can I run Muse Glimmer 30B locally? for every quant.

Read the licence first. Meta says the Muse Model License has “limited commercial terms”. Check it against your use before you spend $44,598.

Test before you buy. Run your real tasks on the API for a week. If Muse Spark Open cannot do your work there, it will not do it at home.

Honest Caveats

1. We did not run the model. Every speed is Apple’s. Every fit claim is Meta’s or Apple’s, or our arithmetic.
2. The 512GB Mac is not shipping yet. Delivery is reported for late November to early December 2026.
3. The API price is from aggregators. Check the provider’s own page before you rely on it. Prices change.
4. Meta and Apple round the checkpoint differently. The file is 533 GB, which is 496 GiB. Meta says “about 530 GB”, Apple says “about 493GB”. It is the same file.
5. The one-Mac routes are untested. We found no published speed for a 3-bit file on one 512GB Mac or a 1-2 bit file on one 256GB Mac.
6. The Musk quote is as reported. We read it in Benzinga’s headline, not in the original post.

Sources

• Meta: Build with Muse Spark Open: parameters, active parameters, context, BF16 and NVFP4 sizes, Mac Studio and GPU setups, GGUF note, licence name, release date
• Meta Research: Muse Spark Open: “most capable US open-weight model”, “limited commercial terms”
• Apple Machine Learning Research: MLX distributed inference: 493 GB footprint, single Mac cannot run it, decode and time-to-first-token table, 65,536-token results
• 9to5Mac, 28 October 2026: $22,299, $29,799, $15,500 memory upgrade, delivery window
• Daring Fireball: M5 configurations and pricing: chip prices, 256GB upgrade, storage upgrades
• Our Mac Studio config picker: $9,499 and $10,799 256GB prices from Apple’s configurator, read 21 September 2026
• Benzinga: the Musk quote
• pricepertoken.com and computeprices.com: $1.25 input, $4.25 output, $0.15 cached input
• CloudYeti: What would it cost to run Claude Opus locally?: July 2026 8x B200 rent

See Also

• Can I run Muse Spark Open locally?: every file size and what fits one machine
• Which Mac Studio to buy for local LLMs: every M5 configuration priced per GB
• Best local LLM for a Mac Studio M5 Ultra 256GB: what one 256GB Mac runs well
• Should you wait for the M5 Ultra Mac Studio?: the launch timeline
• What would it cost to run Claude Opus locally?: the same math for a frontier-class setup
• Can I run Mistral Large 4 locally?: the other 1T-class open model this month
• Local LLM electricity cost and break-even: add your power bill to the table
• Qwen 3.8 27B vs Muse Glimmer 30B: Meta’s small open model, on normal hardware