Running Local AI Models with Ollama: What You Need to Know

Local AI tools like Ollama make it possible to run capable language models entirely on your own hardware — no API keys, no data leaving your machine, no usage limits tied to a subscription.

The main constraint is GPU memory (VRAM), not raw compute. A model that comfortably fits in 8GB of VRAM will run smoothly; one that doesn't will either run painfully slowly on CPU offload or fail to load. Matching model size to available VRAM is the single most important decision before anything else.

NVIDIA GPUs remain the most straightforward path today, largely due to CUDA driver maturity and broader tool support, though that's shifting. Getting drivers, CUDA, and the runtime all aligned correctly is where most local setups actually go wrong — version mismatches between the driver and the toolkit are the most common failure point.

For anyone considering a dedicated AI workstation — whether single-GPU or multi-GPU — the build decisions that matter most are VRAM capacity, power delivery, and cooling headroom, in that order. Raw core count matters far less than most builds assume.