Mini PC vs Desktop for Local AI

Most comparisons of these two treat it as a performance question. It is really a form-factor decision with a capability consequence, and the consequence runs in a direction people find counter-intuitive: the small machine can run larger models than the large one.

Everything else — power, noise, where it can live, whether you can change it in two years — follows from the shape you pick.

The specifications

The row that surprises people is memory. A 4.5-litre box with no graphics card addresses three times the model memory of a tower built around the fastest consumer GPU made. The tower runs what fits several times faster.

Power, over three years

The number that most often decides this once someone works it out.

Assume four hours a day at sustained load and twenty hours idle — a reasonable pattern for a machine that is also your desktop:

Roughly 4,000 kWh of difference — about $650 at $0.16/kWh, or £1,020 at £0.25 — before the cooling load the extra heat adds in summer.

If the machine is an always-on inference server rather than a desktop, the gap widens further, because idle draw dominates and the desktop’s is eight times higher.

Noise, and where the machine can live

Less quantifiable and often more decisive in practice.

A 575 W graphics card under sustained inference is a different acoustic problem from the same card gaming. Gaming load fluctuates; a model generating for twenty minutes holds the card near its power limit for twenty minutes, and the fans hold their speed to match.

A 120 W mini PC is audible under load and not intrusive. It can sit on a desk in a shared room, or in a cupboard, or behind a monitor.

This decides more purchases than performance does. A machine that annoys the household gets switched off, and a machine that is switched off runs nothing.

Upgradeability: the desktop wins outright

The axis where the tower is not merely better but categorically different.

On a desktop, the component that determines capability is the one you can replace. Graphics cards come out; memory and storage are standard parts. A machine bought today around a 16 GB card can hold a 32 GB one in two years without touching anything else, provided you sized the power supply with headroom.

On a unified-memory mini PC, memory is soldered. There is no upgrade path at all — the configuration you buy is the configuration you own for the machine’s life. That is why our mini PC guidance is so insistent about buying 128 GB rather than 64: the decision cannot be revisited.

In a field where model sizes have only grown, that asymmetry compounds over a five-year horizon.

The capability consequence

Which brings us to what each shape can actually run.

Read across the rows and the trade is clear. For anything that fits in 32 GB the desktop is between six and eight times faster. For anything that does not, the desktop scores zero.

The underlying arithmetic is in How Much VRAM Do You Need for Local AI?; the short version is that capacity determines what runs and bandwidth determines how fast.

The practical question is therefore not “which is better” but “is six tokens per second acceptable for what you want to do?” For batch summarisation, overnight processing, or asking a question and coming back — comfortably. For interactive coding, where you are waiting on every response — no.

Total cost, honestly

Purchase price is the least interesting part.

The desktop costs more to run — around $650 over three years at the pattern above — and less to keep current, because you replace one component rather than the machine.

The mini PC costs very little to run and cannot be upgraded, so its replacement cycle is the whole machine. If model requirements grow past what you bought, the answer is a new box.

Which is cheaper over five years depends almost entirely on whether the mini PC’s memory configuration ages well. Buy 128 GB and it plausibly does. Buy 64 GB to save money and you have bought a shorter machine.

What the desktop’s slots buy beyond the graphics card

Upgradeability gets discussed as though the graphics card is the only thing that changes. It is the most important, and it is not the only one.

A tower has spare PCIe slots and drive bays, which over a machine’s life tends to mean:

• A 10-gigabit network card when the storage outgrows the network. On a mini PC that means a USB adapter at reduced speed, or nothing.
• More NVMe drives. Model libraries grow, and a desktop board typically has two or three M.2 slots plus SATA. Most mini PCs have one or two, and they are full.
• A second GPU, if the build was planned for it — the only route to 48 GB on consumer hardware, and therefore to 70B at speed rather than at reading pace.
• A capture card, an HBA, a coral accelerator — whatever the next few years turn out to need.

None of these is essential. Collectively they are the difference between a machine you adapt and a machine you replace.

What happens in year three

Worth thinking about at purchase, because the two shapes age differently.

The desktop is likely still in service with a different graphics card in it. The case, supply, storage and CPU carry over, and the upgrade costs one component. If the supply was sized with headroom, it does not even cost that.

The mini PC is likely still doing exactly what it does today, competently, having cost almost nothing to run. If model requirements have grown past its memory, it becomes a services node — which is a genuinely useful second life, and not the machine you bought it to be.

Neither is wrong. But if you expect your requirements to move, the tower absorbs that and the small machine does not.

The answer many people arrive at: both

Worth stating because it is common, sensible, and rarely presented as an option.

A desktop for interactive work — image generation, fine-tuning, the models you use while you are sitting there — and a small always-on machine as an inference and services node for the large models, the overnight jobs and everything else the household needs running.

Two purpose-built machines frequently cost less than one that compromises on both, and each is good at its job rather than adequate at two. It also means the loud machine can be switched off when you are not using it, which the numbers above make attractive.

If you go this way, the network between them starts to matter — see 2.5GbE vs 10GbE.

The winner, by scenario

The verdict

If your models fit in 32 GB, buy the desktop. It is several times faster, it can be upgraded, and the ecosystem assumes it. Accept the power and the noise as the cost of that.

If you need 70B-class models, buy the mini PC — not because it is better, but because it is the only one of the two that does the job at all, at a fraction of the power. Then be honest with yourself about whether six tokens per second suits how you actually work.

If you can stretch to both, that is frequently the best answer of the three, and it lets each machine be good at one thing.

What we would avoid is buying a mini PC with 64 GB because 128 seemed expensive. That is the configuration that ages worst: too little for 70B, and no path to more.

Continue your research

• Best Mini PCs for Local LLMs — which small machine, if that is the shape
• Best AI Workstations — the tower side in detail
• Mac Studio vs AI PC — the third architecture in this trade
• How Much VRAM Do You Need for Local AI? — establishing which side of 32 GB you are on