Most AI assistants are read-only. They can answer questions, write text, generate images — but they can’t take actions. OpenClaw changes that.
OpenClaw is an agent runtime that connects a language model to real tools: file systems, shell commands, web browsing, messaging, calendars, and more. Run it locally with Ollama as the backend and you have a fully private, agentic AI that can actually do things — without sending a single byte to the cloud.
Choosing a GPU for local AI comes down to one number: VRAM. The more you have, the larger the model you can run, and the faster it goes. Here’s the full breakdown.
Why VRAM matters more than compute When a model is loaded, it lives in VRAM. If your model doesn’t fit, it spills into system RAM — which is 10–50× slower for inference. VRAM is the bottleneck.
Quick reference:
Ollama is the easiest way to run large language models locally. It handles model downloads, quantization selection, GPU detection, and serving — all with a single command. Here’s how to get running in under five minutes.
What you need A machine with at least 8 GB RAM (16 GB recommended) A GPU with 6+ GB VRAM, OR a fast CPU with 32+ GB RAM Linux, macOS, or Windows (WSL2) Step 1: Install Ollama curl -fsSL https://ollama.