Apple just announced its new and updated Mac mini and Mac Studio computers with an explicit reference to helping you save on the LLM tax. Buried in the newsroom copy for the M5 Ultra is this line: the new Mac Studio “lets users run massive models entirely on device with complete privacy — without counting tokens or worrying about rising cloud costs.”
But is it true?
The privacy part: sure. That’s obvious. The math part on “rising cloud costs” is of course, the point in question.
At first glance, it’s very tempting: these machines are beasts. The M5 Ultra tops out at a 36-core CPU, an 80-core GPU, and 1.2TB/s of bandwidth, and it has a price to match those stats: $18,299 with 256GB and 16TB of storage ⟦though it’s the 16TB SSD, not the memory, doing most of that damage⟧. There’s a 512GB configuration landing in late October at an unannounced price. Or you can max out a more consumer-oriented machine, the Mac mini, and you get an 18-core M5 Pro, 307GB/s, and a hard ceiling of 64GB of memory for $6,699.
But $200/month is a lot less than those big numbers.
For every developer who has watched a Claude Code session hit a rate limit at 4PM or opened an OpenAI API invoice and gasped, Apple is selling an escape hatch: buy the box once, run an open-weight model on it and stop renting intelligence by the token.
Does it work? Let me answer that with a definitive maybe.
Here’s the math
Here’s the math, assuming eight hours of electricity per day at 18.44¢/kWh, and rating each machine for its ability to run a local LLM in memory (thank you Claude for the analysis).
| Config | Price | 3-yr total | Per month | Payback vs. $100/mo | Payback vs. $200/mo | Biggest model it holds | Local LLM score |
|---|---|---|---|---|---|---|---|
| Mac mini M6, 32GB | $1,299 | $1,389 | $39 | 1.1 yrs | 0.6 yrs | Qwen3.8-27B (16GB, 4-bit) | 3/10 |
| Mac mini M5 Pro, 64GB | $2,699 | $2,879 | $80 | 2.4 yrs | 1.2 yrs | Qwen3-Coder-Next 80B (47GB, 4-bit) | 5/10 |
| Mac Studio M5 Max, 128GB | $4,799 | $4,988 | $139 | 4.2 yrs | 2.1 yrs | gpt-oss-120b (63GB) | 7/10 |
| Mac Studio M5 Ultra, 256GB | $9,499 | $9,843 | $273 | 8.8 yrs | 4.2 yrs | DeepSeek-V4-Flash 284B (~188GB) | 9/10 |
| Claude Max / ChatGPT Pro | — | $3,600–$7,200 | $100–$200 | — | — | Frontier: Opus 5, GPT-5.6 Sol | — |
The Mac Studio with M5 Max opens at $2,499 with 36GB of unified memory and up to 614GB/s of bandwidth. The M5 Ultra starts at $5,499 with 96GB and 1.2TB/s, a 50% bandwidth jump over the M3 Ultra it replaces. Memory is expensive — thank you AI data centers — with a 128GB Max running about $4,800 specced, and 256GB on the Ultra landing at $9,499. Both ship September 22.
(The 512GB Ultra doesn’t arrive until late October and Apple hasn’t priced it, so it was challenging to add here.)
The Mac mini is the cheap seat: $899 for the new 2nm M6 at 170GB/s, $1,699 for the M5 Pro at 307GB/s. They cap at 32GB and 64GB respectively, which matters if you want to run AI inference locally.
On the subscriptions side, Claude Max is $100 or $200 a month depending on tier. ChatGPT Pro is priced identically. GitHub Copilot Max is $100.
Against $200 a month, break-even on a 128GB M5 Max Studio is about two years. On a 256GB Ultra, about four. The $2,499 base Studio breaks even in 12.5 months ⟦a config not in the table above⟧, but 36GB won’t run the models this article is about.
Electricity barely moves the needle. Apple hasn’t published detailed numbers for the M5 generation yet, but the M3 Ultra Studio it replaces peaks at 270W and idles at 9W. At the U.S. average of 18.44 cents per kWh, running one eight hours a day, five days a week costs roughly $10 a month. Max it out 24/7, and you’re spending $36 a month: minimal.
So a 128GB M5 Max Studio has a three-year cost of ownership near $5,000, which is about $140 a month. That is less than $200-a-month Claude Max, but more than the lower-level Claude Max option at $100 a month. Interestingly, Anthropic’s documentation says that for metered API spend in enterprise deployments Claude Code costs “around $13 per developer per active day and $150-250 per developer per month,” with 90% of users under $30 a day.
The Mac lands inside that band.
But there’s a problem
It’s not just about buying the Mac. You have to actually run something on it.
Good news: Moonshot’s Kimi K3, released in July, currently sits at number one on WebDev Arena’s blind human-preference leaderboard. It’s the first open-weight model ever to top it.
Bad news: Kimi K3 is 2.8 trillion parameters. In its native MXFP4 format, that’s roughly 1.4TB of weights. It does not fit on the so-far-unpriced 512GB Mac Studio, and it does not even fit on two of them. Other options? DeepSeek’s V4-Pro-0813 checkpoint is 893GB. Alibaba’s flagship Qwen3.8 is 2.4 trillion parameters. The problem is that the frontier of open models is moving at the same time as the hardware companies like Apple are releasing new hardware.
Apple giveth and AI taketh away, to reinterpret the old Intel giveth and Microsoft taketh away line.
What fits in 128GB is a tier down: Qwen3-Coder-Next, an 80B mixture-of-experts model with 3B active parameters, at roughly 47GB in 4-bit. OpenAI’s gpt-oss-120b at about 63GB also fits, as does Qwen3.8-27B at 16GB. These aren’t bad models. But will they do the job?
On the independently-run Terminal-Bench 2.1 leaderboard, Claude Code paired with Anthropic’s Fable 5 scores 83.8%. The only open-weight entry on the entire board is GLM-5.1, at rank 17 with 58.7% … and GLM-5.1 is a 754B model that needs roughly 420GB just to hold at 4-bit.
It is not running on your desk.
Even worse, from Qwen’s own research paper: on Terminal-Bench 2.0, Qwen3-Coder-Next driven by Claude Code scores 30.9%, against Claude Opus 4.5’s 53.9% in the same table. Not wonderful: at the end of the day you need good output to make all of this AI code generation worthwhile.
And then there’s speed
Agentic coding is not like chatting with AI. It’s much more prompt-processing-heavy, because every turn re-sends the system prompt, the file contents, the tool results, the code currently being worked on, the details the LLM needs to make the code function in the context of the app, and so on.
Apple silicon is fast at generating tokens, but it has typically been slow at ingesting them, because that’s compute-bound, and an M3 Ultra has roughly a quarter the FP16 throughput of an Nvidia DGX Spark.
One developer running Claude Code against a local model on a 512GB M3 Ultra reported that a 16,000-token system prompt took about 90 seconds before the first token appeared. Another, on a 48GB M4 Pro laptop, measured time-to-first-token at 20 seconds cold and about a minute deep into a coding session — fine, he concluded, for “considered, spec-driven, review-heavy engineering,” useless for “hundreds of low-latency interactions a day.”
Apple is attacking this problem.
Apple’s own machine learning researchers benchmarked a base M5 MacBook Pro against an M4 and found time-to-first-token improved 3.3x to 4.1x while token generation improved only 19-27%. Apple claims up to 4x faster LLM prompt processing on the M5 Ultra versus the M3 Ultra. That’s better, but it’s not necessarily speedy.
And we have to see it in the real world to really believe it.
The argument that isn’t about money
The strongest case for buying one of Apple’s new Mac Minis or Studios isn’t actually about saving money on LLM token spend.
It’s privacy.
Bart de Witte, CEO of Berlin-based Isaree and a longtime advocate for open medical AI, wrote after the announcement that the M5 Ultra “holds a frontier-class medical AI model, the kind that today ‘requires’ a data center, entirely in local memory. No API calls. No tokens. No patient data leaving the building.” His conclusion: “For a decade, the industry told hospitals: ‘AI means sending your data to someone else’s computer.’ The hardware just ended that argument.”
He’s right about the hardware and honest about the rest: “what remains is software, and that’s the hard part.”
For developers under GDPR, HIPAA, ITAR, or a client contract that forbids third-party inference, a Mac Studio isn’t a cost play. It’s a privacy play.
And using a local LLM for chat – even medical chat – is almost certainly going to be much more efficient and fast than agentic coding.
So here’s the verdict
If you’re paying $200 a month and you’d spend $4,800 on a 128GB M5 Max, you save money in year three … while running models measurably weaker at exactly the agentic work you’re buying the subscription for.
I’m not sure that’s a win.
In fact, I’m pretty sure it’s not.
But, buy the Mac if your code can’t leave the building, if you want to run overnight batch work without watching a meter, or if you’d have bought a fast Mac anyway and local inference is just an added bonus.
Don’t buy it expecting to cancel Claude or ChatGPT. I run an agent on my Mac mini, and it does just well using up Claude’s five-hour limits. I don’t expect it to run a large local LLM as well.