What to look for when running AI locally
Choose the software and models you want to use before choosing the computer. Hardware compatibility and usable memory are essential.
Choose a model and inference tool first. Then check whether a particular mini PC can run that workload on the operating system and hardware you intend to use.
Start with your software stack
Identify the runtime, model, quantization and operating system before buying hardware. GPU support varies by vendor, driver and backend; an “AI PC” badge is not proof that your preferred tool will use the GPU. Check the tool's current hardware list for the exact device.
Budget usable memory, not just installed RAM
Model weights are only part of memory demand. Context length and concurrent requests also matter, while the operating system and other applications need space. A larger context can require more memory; avoid a universal RAM minimum for every model and workload.
Separate prompt and generation speed
Tokens per second can describe prompt processing or generated output, which are different tests. A result is useful only with the model, quantization, context, runtime version, backend and exact system configuration attached. Even a named benchmark may omit tokenization and sampling time.
Plan for your real workflow
A single local chat, coding assistant, document search setup and multi-user server stress memory differently. Decide whether you need simultaneous requests and how long your prompts will be. Confirm network, storage and cooling requirements for a machine that may run continuously.
Treat empty results honestly
MiniPC.guru shows a benchmark only after its source and test conditions have been reviewed for the exact configuration. If a profile has no result, the specifications are still useful for filtering, but they are not a measured tokens-per-second claim.