Local large language models have moved from hobby project to everyday tool for a lot of our customers. Developers, writers and small businesses want to keep their data on their own hardware. That has created real demand for compact "AI-ready" systems.
Why unified memory matters
Traditional desktops keep the model in the graphics card's VRAM, which typically tops out at 16–32GB on consumer cards. Chips like AMD's Ryzen AI Max series share a large pool of fast system memory with the integrated graphics. That means a 64GB or 128GB mini PC can load models that would not fit on most gaming GPUs.
What to expect
- Small models (7–14B): quick and responsive on almost any modern AI mini PC.
- Medium models (20–34B): very usable on 64GB systems with sensible quantisation.
- Large models (70B+): they load and run, but slowly. Fine for batch jobs, less so for chat.
When a full workstation makes more sense
If you are fine-tuning, serving several users or need the fastest possible token generation, a desktop with a high-VRAM NVIDIA card is still the better tool. CUDA support across AI frameworks is hard to beat.
Not sure which route suits your workload? Our Deep Learning team will happily talk it through.