The Best Mini PC for Running Local LLMs in 2026

Last updated 29 August 2026. Prices current at time of writing; every machine below is one we stock.

Most “best mini PC” articles rank machines by processor. For running language models locally, that is the wrong first question.

Memory decides what you can run. Everything else decides how pleasant it is.

A model that does not fit in memory does not run slowly — it does not run. So the honest way to choose is to work out the largest model you actually want, size the memory for it, and only then argue about processors.

How much memory do you actually need?

At 4-bit quantisation — what most people run locally — weights come in near half a gigabyte per billion parameters. That gives a workable rule of thumb:

Two things this table hides, both of which catch people out:

Context costs memory too. The weights are the floor, not the ceiling. Long context windows can add many gigabytes on top, and it is usually context — not the model — that triggers the first out-of-memory error.

Memory bandwidth sets your speed. Two machines with 64GB can differ enormously in tokens per second. Capacity determines whether it runs; bandwidth determines whether you enjoy using it.

The recommendations

Start here if you are new to this — Beelink EQi Pro, $629

Beelink EQi Pro — Ryzen 7 7735HS, 24GB LPDDR5, 1TB SSD, dual 2.5GbE, and OpenClaw pre-installed on Ubuntu.

That last part is the whole argument. The most common way local-AI projects die is the buyer losing a weekend to CUDA versions and Python environments and quietly giving up. This one boots into a working setup.

24GB comfortably handles 7B–14B models. That covers a genuine majority of practical local use: coding assistance, summarising documents, chat that never leaves your network.

Note it ships with Ubuntu, not Windows. If you want Windows on the same silicon, the Beelink EQR7 is $479 — but you are then setting the software up yourself.

The sensible middle — Beelink GTi15 Ultra, from $1,499

Beelink GTi15 Ultra — Intel Core Ultra 9 285H, configurable to 64GB.

Configured with 64GB this reaches 70B-class models, which is where local output starts feeling genuinely competitive with hosted services for everyday work. It is also a fast, quiet desktop, so it earns its space when you are not running inference.

For most people willing to spend four figures, this is the machine — not the $4,000 one.

The no-compromise pick — Beelink GTR9 Pro, $4,349

Beelink GTR9 Pro — Ryzen AI Max+ 395, 128GB LPDDR5x-8000, dual 10GbE, vapour-chamber cooling.

128GB of fast unified memory in a chassis this size is the reason to buy it. It puts very large models on your desk, and the dual 10GbE means it slots into a real network rather than bottlenecking on a single gigabit port.

If you live in NVIDIA’s world — DGX Spark, $4,699

NVIDIA DGX Spark — GB10 Grace Blackwell, 128GB unified memory, 4TB NVMe.

If your pipeline assumes CUDA, this removes an entire class of problem. It is an appliance rather than a desktop, and it is worth every penny to the right buyer — and poor value to the wrong one. We compared it directly against the GTR9 Pro if you are torn.

Buy the memory now

One piece of advice that matters more this year than most: in almost every machine here, memory is soldered. The configuration you buy is the configuration you own for the life of the machine.

That normally argues for buying a little more than you need. In 2026 it argues for it strongly, because DRAM prices have risen roughly 700% in a year and analysts do not expect relief before 2028. The 64GB you skip today is not cheaper in twelve months.

What we would tell a friend

Most people asking this question do not yet know which models they will actually use day to day. If that is you, buy the $629 EQi Pro, run your real workload for a month, and let it tell you what you need. You will make a far better $4,000 decision afterwards — or discover you never needed to spend it.

If you already know you are running 70B models daily, skip the middle and buy the memory.

Tell us the models you want to run and how you work, and we will tell you which of these fits — including when the answer is the cheaper one. support@computahardware.com.