Best mini PC for local AI (2026)
Updated June 2026
You don't have to build a GPU rig to run big models at home. Unified-memory mini PCs and appliances pack 128GB into a small box — trading raw speed for the capacity to fit models a single GPU can't. Here's how the main options compare.
Appliance vs GPU build
A discrete GPU is the fastest way to run a model that fits in its VRAM, and it's upgradeable — but VRAM is scarce and pricey in 2026. A unified-memory mini PC shares one big pool of memory between CPU and GPU, so it can hold far larger models than a single consumer card, in a small, quiet, low-power box. The trade is bandwidth: unified LPDDR5x is high-capacity but slower than GPU GDDR, so big models fit but generate more slowly.
The contenders
| Memory | Bandwidth | Price | Best for | |
|---|---|---|---|---|
| AMD Strix Halo Ryzen AI Max+ 395 | up to 128GB unified | ~256 GB/s | ~$2,300-3,300 | Best value capacity; gaming too |
| NVIDIA DGX Spark | 128GB unified | ~273 GB/s | ~$4,699 | CUDA toolchains, long prompts, multi-user |
| Apple Mac Studio / mini | unified, up to 128GB+ | high (M-series) | varies | Smoothest software (MLX / Ollama) |
| RTX GPU build | VRAM per card (16-32GB) | very high (GDDR) | see tracker | Max speed if the model fits |
Capacity and bandwidth figures are approximate and vary by SKU; check current listings before buying.
How to choose
- Most capacity per dollar: a Strix Halo mini PC (e.g. GMKtec EVO-X2) — 128GB unified for well under the DGX Spark.
- CUDA / multi-user / long context: NVIDIA DGX Spark.
- Best software experience: Apple Mac (MLX and Ollama are very smooth).
- Fastest single-model speed: a discrete RTX GPU that fits your model in VRAM — pair with the best-GPU guide.
Local-AI mini PC FAQ
Mini-PC appliance or a GPU build for local AI?
A GPU build is faster when the model fits in VRAM and is more upgradeable, but VRAM is scarce and expensive. A unified-memory mini PC (Strix Halo, DGX Spark, Mac) trades raw speed for huge memory capacity in one small, low-power box — so it can hold much larger models than a single consumer GPU, just slower. Pick the appliance for capacity and simplicity, the GPU for speed.
What is the AMD Strix Halo (Ryzen AI Max+ 395)?
An APU with up to 128GB of LPDDR5x unified memory (around 256 GB/s) shared by the CPU and a strong integrated GPU. In mini PCs like the GMKtec EVO-X2 it runs roughly $2,300-3,300 depending on memory, making it the value leader for fitting large models locally — though its Linux/ROCm software stack is still maturing.
DGX Spark vs Strix Halo vs Mac?
DGX Spark (128GB unified, ~$4,699) wins on CUDA toolchains, long-prompt ingest and serving multiple users. Strix Halo gives similar capacity for less money. Apple Mac Studio/mini offer the smoothest software (MLX/Ollama) at comparable token speeds. All three are about fitting big models, not maximum speed.
How fast are these on a 70B model?
Unified-memory appliances typically deliver single-digit to low-double-digit tokens/sec on 70B-class models at Q4 — usable for interactive single-user work, slower than a GPU that fully fits the model. DGX Spark pulls ahead on long prompts and batched/multi-user workloads thanks to CUDA.