AI hardware ROI for an Apple Silicon local-model system: depreciation and resale value
Unified memory on Apple Silicon is fixed at the point of sale. Whatever configuration you buy is what you have for the machine's life: no slot to add capacity later, no swapping in more memory when next year's model wants more than you budgeted for.
This is about a Mac mini, Studio, or MacBook configured to run local models: enough unified memory and storage for the target models plus ordinary work. The workload is quiet local development, document analysis, MLX experiments, and larger models that need one shared memory pool. The alternative is a discrete-GPU workstation plus a laptop, or hosted APIs. You get low-noise integrated hardware, idle efficiency, and one machine someone still supports.
None of that requires failure. Capacity assumptions age, platform support shifts, workload fit drifts, and any one alone can end a machine's usefulness while it still boots fine on your desk.
What actually eats the resale value
The risk here is memory you can't touch after purchase, capacity tiers priced at a premium over the tier below, runtime support tied to one platform's toolchain, and a resale market that thins out once your configuration sits a step from what most buyers wanted.
Workload fit is the piece people treat as permanent. It isn't: the same reasoning behind buying for local inference, the local-first cascade, can turn against you once a model's memory needs outgrow the tier you bought.
Tie useful-life to the exact configuration you own, not the product line: model growth, platform compatibility, warranty term, and what the resale market pays for that memory-and-storage combination this month. A long tax depreciation schedule isn't evidence of competitiveness, just an accounting convention.
Split the bill before you trust it
Annual capital cost is installed cost minus a conservative resale estimate, divided by the years you expect to use it, not the years it could keep running. Model early replacement and resale separately from whatever your accountant depreciates.
Split fixed from variable. The machine, memory tier, and reserved capacity are fixed once bought; electricity, paid tokens for whatever the box doesn't cover, data transfer, and support move with use. Human review sits in between: fixed if it's a dedicated reviewer, variable if it's per-exception correction scaling with error rate.
List every incremental item in the bill of materials, then subtract whatever you'd have bought anyway. A laptop you already use isn't an AI cost; a memory upgrade bought specifically to fit a model is.
Measure the workload across local hardware, rented instances, and hosted APIs, on the same terms:
- accepted tasks and outright failures, not token counts
- input, output, and reasoning-token volume
- retries, abstentions, human repair minutes
- queue, first-token, completion time, and p95
- active, idle-resident, asleep, and unavailable hours
Quality adjustment matters: a cheap model needing two attempts and five minutes of correction is only cheap on paper, price the finished result, not the first draft. And don't price a local specialist that clears its task against a frontier API cranked to its most expensive reasoning setting. That's not a fair fight.
The month the machine pays for itself
Payback month is the first month cumulative discounted benefit clears cumulative cost; ROI over your horizon is net discounted benefit over discounted cost, both against a conservative useful life and resale figure. Hardware can keep running after model growth or dropped software support makes it economically obsolete, and obsolete is not broken; only one shows up in a depreciation schedule.
Productivity benefits need a realization factor: saving a developer ten minutes doesn't hand the business ten minutes of billable output. Count it only when it avoids a hire, cuts outsourced spend, increases what ships, or shortens a binding queue, and run the whole model once with that value at zero. A purchase clearing the bar only on savings you can't cash in is a hunch, not a plan.
Compare against renting too, alongside buying and APIs. Hourly GPU rental earns its keep while demand and hardware shape stay uncertain: persistent storage, image prep, transfer, minimum billing, setup time, and the idle instance someone forgot to shut down. The same instinct that makes a subscription the wrong unit for AI cost applies here: a short rental benchmark before buying is cheap insurance against an expensive wrong purchase.
Base the whole thing on the shorter of financial life and workload-relevant technical life, as a monthly break-even: accepted tasks per month, productive hours, a maximum API cost per task, or a minimum useful life. Retest when volume changes materially, a new model resets the quality bar, API prices move, the electricity contract changes, or a component hits end of support.
If you keep exactly one rule out of all this: age the machine by what it can still do for the workload in front of you, not by what you paid for it or what the warranty card promises.