Rent the model, or lease the machine?

On Monday Apple announced the new Mac Studio, and buried under the usual keynote language about "frontier-class models on your desk" is a line that matters more than the chip specs: you can lease one for $48.99 a month.

Read that next to what most of us actually pay for AI. ChatGPT Plus is $20 a month. Claude Pro is $20, or $17 annual. SuperGrok is $30. The Pro tiers run $100 to $200. For the first time, the monthly cost of owning the machine overlaps the monthly cost of renting the intelligence.

That overlap is new, and it is worth taking seriously. I have been running models on my own hardware for a while now, and I planned my last hardware purchase around exactly this question: what does it take to stop depending on someone else's API for the work that matters? So this issue is the honest version of the math. What local AI actually costs, what it actually does, and where the subscription is still the right answer.

One warning before the numbers: that $48.99 headline lease is bait. I'll show you why.

What "local AI" actually means

When you pay OpenAI, Anthropic, or xAI twenty bucks a month, you are renting time on their models, running in their data centers, under their rate limits, on their terms. The model can change under you. The price can change under you. In February, OpenAI started showing ads to its Free and Go tiers. The direction of travel is not subtle.

Local AI means the model runs on a machine you control. The models are open-weight releases (gpt-oss, DeepSeek, Qwen, GLM, Kimi) that you download once and run forever. Nobody meters your tokens. Nobody reads your prompts. Your customer list, your pricing, your contracts never leave the building. If the vendor triples the price or has an outage on the morning you need to send quotes, your machine does not care.

The catch has always been the hardware. Running a model worth using takes a lot of fast memory, and until recently the machines that had it were server-room purchases. What changed Monday is that Apple is now selling, and leasing, a small silver box built specifically for this, with up to 512GB of unified memory and the bandwidth to feed it.

The actual dollars

Here is what the subscriptions cost, from the vendors' own pages, as of this week:

And here is what the machine costs. These are real configurator prices from Apple's store, buy price and the 36-month Apple Upgrade lease:

Now the warning I promised. The $48.99 build has 36GB of memory, and memory is the whole game in local AI. It decides which models you can load at all. A 36GB machine runs small models, the 20B class, fine. Those are genuinely useful for drafts and summaries, but they are not the models this article is about, and if you lease the base build expecting a ChatGPT replacement you will box the machine up inside a month.

The builds that matter start at 64GB and get honest at 128GB. And at the top, it gets expensive fast: the 256GB upgrade alone is $4,000, and the 512GB version lands in late October at a price Apple has not published, with informed guesses starting well above $10,000. Nobody reading this needs the 512GB machine. It exists for people who want to run the largest open models ever released, and it is priced like it.

Add electricity, since we are being honest about actual dollars: the Mac Studio draws 480 watts flat out and far less at idle. Work it hard every day and you are adding maybe ten to twenty dollars a month to the power bill. Not nothing, not a factor.

So the real comparison is this: a machine worth owning costs $75 to $110 a month on lease. That is not the $20 subscription band. It is the $100 Pro band. And the 256GB monster leases for $195.46, four dollars under a ChatGPT Pro subscription. Apple priced the lease ladder right on top of the AI subscription ladder, and I do not believe that is an accident.

The actual performance

Here is the part the YouTube reviews skate past. The dollars only matter if the machine does the work, so let's be specific about what each build runs.

First, one piece of jargon that earns its keep: mixture-of-experts, or MoE. Almost every serious open model released this year is built this way. The model might have 284 billion parameters total, but it only activates 13 billion of them for any given token. The catch is that all 284 billion still have to sit in memory, because the model decides which experts it needs token by token. That is why memory is the whole game and why the Mac Studio's unified memory matters more than raw GPU power: the machine that can hold the model wins, and once it is held, the small active count keeps it fast enough to use.

That architecture is also why the open-model story changed this summer. The interesting releases are no longer American; they are a wave of Chinese open-weight MoE models shipping weeks apart, and they are what these memory tiers actually exist to run:

A 64GB build runs the honest small end: OpenAI's gpt-oss-120b just fits at its native compression, and dense workhorses like Qwen's new 27B (Apache licensed, reads images too) run with room to spare. A 128GB build is where it gets real: DeepSeek V4 Flash, a 284B-parameter MoE with a million-token context and an MIT license, runs quantized in roughly 90 to 110GB, and it is the reason the 128GB question dominates r/LocalLLM this week. The 96GB Ultra trades a little model ceiling for double the memory bandwidth (1.2TB/s against the Max's 614GB/s), which roughly means tokens come out twice as fast on whatever fits. The 256GB Ultra opens the next shelf: GLM-5.3-Flash (320B total, 18B active) at quality settings, Qwen's 397B class, and even heavily compressed builds of Moonshot's trillion-parameter Kimi K2.5, which Moonshot's own quant partners say runs on a 256GB Mac. Community numbers put these big MoE models at usable reading speed or better on the new machines, and Apple claims the new Neural Accelerators dramatically cut prompt processing time, historically the Mac's weak spot on long documents.

Quality is the question that matters, and the honest answer moved this year. Z.ai's GLM-5.3 posts frontier-class coding and agent benchmarks, trading wins with Claude and GPT on some tests. DeepSeek V4 Flash and GLM-5.3-Flash sit a rung below that but within sight of last generation's frontier. Two honest caveats before you get excited. Those are vendor-published benchmarks, not independent replication, and benchmark charts are marketing until outside testers confirm them. And the GLM-5.3 flagship is not something you can download yet: Z.ai ships weights about two weeks after launch, and at roughly 750B parameters it is a 512GB-machine model when it lands, not a $5,500-machine model.

So the working rule still holds, just with a smaller gap than a year ago: the models you can run on a 128GB machine handle drafts, summaries, classification, and document Q&A well enough that you will rarely feel the difference on routine work. You will still feel it on the hard stuff, the long multi-step reasoning and gnarly code where the difference between 90% right and 99% right is the whole value. The $200 Pro tiers are selling you the frontier, and anyone who tells you a Mac Studio replaces ChatGPT Pro today is selling you a Mac Studio.

There is a sharper version of this argument and it deserves airtime. The most upvoted skeptic take in this week's r/LocalLLaMA cost thread runs the math from the other direction: $10,000 of hardware money buys literally billions of tokens through cheap API providers. If raw tokens per dollar is your only metric, the cloud wins and it is not close. The machine only makes sense when the things the token math ignores start being worth money to you: privacy, predictability, and unmetered volume.

Where the machine wins anyway

Three cases, and they happen to be the three that matter most to the people reading this.

First, your data never leaves. If your business touches contracts, financials, health information, client files, anything you would not paste into a random website, local is the difference between "we reviewed the vendor's privacy policy" and "it physically cannot leave the building." One is a promise. The other is a fact.

Second, flat cost at unlimited volume. A subscription meters you, and the meter is exactly what stops people from giving AI the high-volume boring work: classify every email, summarize every call, first-draft every follow-up, re-check every invoice. On a machine you own, the ten-thousandth task costs the same as the first. Zero. The subscription trains you to save AI for special occasions. The machine lets you spend it like water.

Third, agents. This is the one I would watch. The whole industry is moving toward AI that works in the background on long-running jobs instead of answering one question at a time, and background agents chew through tokens at a rate that makes metered pricing genuinely painful. An agent grinding around the clock on your machine costs you electricity. The same agent on a metered API is a variable bill you get to discover at the end of the month.

My position

The premise I started with, and the reason I wrote this issue: we are getting close to local AI being viable for a normal business, and the lease is what changes it. Not because leasing is cheaper than buying (over 36 months it is not), but because it converts the decision from "wire $5,500 into a hardware bet on a market that moves every quarter" into "add a line item that competes directly with a subscription you already pay, with an exit."

Close is not there. Here is where I actually land.

If your AI spend is $20 to $50 a month and it covers your needs, keep the subscription. You are the person the subscription is built for, and the frontier models you get for that money are a screaming deal. Do not let a YouTube thumbnail talk you into a lease.

If you are already at the $100+ tier, or your volume keeps bumping into rate limits, or you have real privacy constraints, the machine is now a legitimate line on the spreadsheet. Not instead of your subscription. Alongside it, with the boring high-volume private work moving local and the frontier work staying rented. That hybrid is what I run, and the machine earns its keep.

And if you are neither: do nothing, but put a tripwire in your calendar. The open-weight models get better every quarter, the gap to the frontier narrows every quarter, and the hardware now leases for subscription money. The month a locally-runnable open model handles your actual daily work is the month this math flips for good, and that month is coming a lot sooner than it looked a year ago.

Below the line for Operators: the exact builds I would order and the ones I would not, which models to run on each memory tier, the software stack in the right order, and a copy-paste brief you can hand your own AI to stand the whole thing up and prove it earns the lease payment in 30 days.

The playbook

Everything above was the argument. This is the shopping list and the install.

The builds, ranked

The workhorse: Mac Studio M5 Max, 128GB, 1TB. $5,399, or $108.02/mo leased. This is the buy. It runs gpt-oss-120b with headroom, it runs the 70B class at high quality settings, and it is the cheapest build where you will not spend the next year wishing you had bought more memory. If you order one machine off this issue, order this one.

The speed play: Mac Studio M5 Ultra, 96GB, 1TB. $5,499, or $110.10/mo leased. Same money, different bet: you trade 32GB of model ceiling for double the memory bandwidth, which is double the tokens per second on everything that fits. Pick this over the workhorse only if you already know your daily model fits in 96GB and responsiveness matters more than ceiling. For agent work where the machine grinds unattended, take the 128GB Max instead; the agent does not care about latency, and the ceiling buys you next year's models.

The volume machine: Mac Studio M5 Ultra, 256GB, 1TB. $9,499, or $195.46/mo leased. The 300B-plus MoE class at quality settings, serious batch throughput, and room to run two big models side by side. The lease costs four dollars less than ChatGPT Pro's top tier. (The 80-core GPU version of the same build is $10,799, or $224.16/mo; the extra $29 a month buys faster prompt processing, not a bigger model ceiling, so skip it unless you are feeding it long documents all day.) Buy this tier only if you have a proven, running local workload that is already saturating a smaller machine or a real compliance requirement. Do not buy it as your first local machine.

Do not order: the 36GB base at $2,499. The lease reads great and the memory strands you in small-model territory. If $2,499 is the budget, you want a different product entirely: the new Mac mini M6 at $899 with 32GB is an honest little machine for 20B-class draft work, and it costs a third as much. The worst outcome in this whole article is paying Studio money for mini capability.

Do not pre-order: the 512GB Ultra. No published price, late October ship, and estimates north of $10,000. Never pre-order what you cannot price. If your workload genuinely needs it, you will still be able to buy it in November.

Lease or buy? Run the actual numbers first: 36 payments on the 128GB workhorse total about $3,889 against a $5,399 sticker. The difference is not interest; it is the machine's residual value, because at term you hand it back. Apple's own FAQ confirms you never pay more than full price, and the buyout option is full price minus what you have already paid. So buying wins if you keep machines four-plus years and want the resale value; the lease wins if you want the exit, the predictable line item, and the walk-into-the-next-chip upgrade path, which in a market moving this fast is worth something real. Treat the lease exactly like the subscription it is priced to compete with: reviewed annually, ended the year it stops earning. Either way it is business equipment doing business work; have the ownership and usage conversation with your accountant before you order, not at tax time.

What to run on it

Match the model to the memory, biggest first. As of this week, on current open releases:

• 64GB Max: gpt-oss-120b at its native compression as the daily driver; Qwen3.8-27B as the fast utility and vision model. This tier is honest but snug; you will feel the ceiling.
• 128GB Max: DeepSeek V4 Flash (284B MoE, MIT license, million-token context) as the daily driver, in a quantized build around 90 to 110GB. gpt-oss-120b as the second opinion, Qwen3.8-27B kept loaded for instant cheap tasks. Aggressive quants of GLM-5.3-Flash also squeeze into this tier if the coding benchmarks hold up under independent testing.
• 96GB Ultra: gpt-oss-120b and the 27B at roughly double the speed of the Max. DeepSeek V4 Flash's quality builds do not fit here; that is the tradeoff you accepted for the bandwidth.
• 256GB Ultra: GLM-5.3-Flash at quality settings, Qwen's 397B-class MoE, DeepSeek V4 Flash with maximum headroom, and compressed builds of Kimi K2.5, the trillion-parameter model, per Moonshot's own guidance for 256GB Macs. Run a big model and a small one side by side.
• Any tier: keep one small fast model resident for classification and routing. Most of your volume should hit the small model; save the big one for work that deserves it.

Watch for two drops that will reshuffle this list within weeks: the GLM-5.3 flagship weights (frontier-class benchmarks, ~750B, realistically a 512GB-machine model), and whatever answers it. Models move monthly; the memory tiers do not. Re-check the r/LocalLLaMA front page in any given week for the current best release per size class.

The stack, in order

1. LM Studio first. Free, graphical, and it makes model download, quantization choice, and "is this machine actually working" a point-and-click exercise. It also runs Apple's MLX format, which community testing consistently clocks meaningfully faster than the generic format on Apple Silicon.
2. Ollama once things work and you want it running as a background service your other tools call.
3. Both expose an OpenAI-compatible local endpoint, which is the important part: nearly every AI tool that accepts a custom endpoint can point at your machine, and your agent tooling works against localhost with a one-line config change.
4. Skip llama.cpp, MLX-direct, and vLLM until you have a reason. You can go your whole local-AI life happily without compiling anything.

The brief to hand your agent

Copy, paste, done. Works with any capable agent that can run commands on the Mac:

Set up local AI inference on this Mac Studio. Steps: (1) Report total unified memory. (2) Install LM Studio, then install Ollama as a background service. (3) Based on memory, pull the current best open-weight general model that fits in roughly 60% of total memory at 4-bit, plus one small ~20B-class model; prefer MLX builds where available. (4) Verify the OpenAI-compatible endpoint responds on localhost. (5) Benchmark both models: report tokens per second generating a 500-word draft, and time-to-first-token on a 3,000-word input document. (6) Route one recurring draft-grade job of mine through the local endpoint and keep the frontier-model routing for everything else. (7) Give me a one-page report: what is installed, what runs at what speed, what job is now local, and what it would take to move the next job. If any step fails, stop and report rather than working around it silently.

The 30-day kill test

The machine is an employee on probation. Between now and day 30, it needs to prove three things: a real recurring job runs on it end to end, the output quality on that job is indistinguishable from the subscription's, and it processed enough volume that metering it would have cost real money or real privacy. Log it honestly for the month. If it passes, expand its duties. If it fails, sell the machine (Apple Silicon holds resale unusually well) or end the lease at term, and go back to renting with a clear conscience and actual data. What you must not do is what most people do with hardware guilt: keep it plugged in doing nothing, telling yourself you will get to it. The machine either earns the $108 a month or it goes.

The tripwires that change this verdict

Put these in the calendar, quarterly:

1. The gap check. Take your hardest real weekly task and run it against the current best open model. The month local output matches your subscription's output on your actual work, the hybrid tilts local.
2. The 512GB price. If October's number comes in aggressive, the 256GB build reprices overnight, and so does the used market under it.
3. Your own meter. The month your subscription tier stops covering your volume, re-run this issue's math before you upgrade tiers. A $100 tier jump and a $108 lease are the same money, and only one of them ends with you owning the means of production.

Hit reply and tell me where you landed: renting, leasing, or waiting. I read every one.