Choose a local-AI computer

How to choose a computer for local AI

Start with the workload, then choose memory capacity and speed. System RAM determines which models and working files the computer can hold. Dedicated GPU memory, or VRAM, has a larger effect on how easily and quickly many image, video, and language-model workloads run.

• Local language models: prioritize enough RAM to hold the model, context, and operating system. A 64 GB computer is a flexible middle tier; 128 GB creates room for larger quantized models and heavier offload.
• ComfyUI images and video: prioritize VRAM when speed and simple workflows matter. Eight GB is a practical entry point for carefully configured jobs, while more VRAM reduces offload and compromises.
• Upgradeable desktops: check memory slots, power-supply capacity, GPU clearance, and the maximum supported RAM before buying.
• Unified-memory systems: shared capacity can fit unusually large models, but capacity alone does not guarantee the speed of a high-end discrete GPU.

Read how HiRAM researches, selects, and updates recommendations.

What our listings mean

“In stock” means the retailer page was available at our latest check; it is not a guarantee of availability when you visit. Compare the exact delivered configuration—not only the model family—because sellers often reuse one product page for several RAM, storage, and GPU options.

HiRAM may earn a commission from qualifying purchases. Compensation does not determine the specifications shown or whether a computer qualifies for a focused guide.