A100: $10,000+. RTX 4090: $1,600. My multi purpose RX 7900 XTX: $800.
It runs a 30B parameter AI model at 50 tokens per second, 24 hours a day, 7 days a week. It powers Mr. Peepers, my autonomous AI agent that manages my home infrastructure, writes reports, and orchestrates tasks across my network.
When people hear "local AI," they think you need NVIDIA enterprise hardware. You don't. AMD's ROCm stack has matured significantly, and the 7900 XTX with its 24GB of VRAM is a genuine workhorse for inference workloads.
The model (qwen3-coder:30b) fits comfortably in VRAM with room for context. Response quality is comparable to cloud APIs for the tasks I need — infrastructure management, code generation, technical writing, and research.
My monthly cost: the electricity to run it. No API metering. No rate limits. No data leaving my network. No surprise bills when my agent decides to do 200 API calls at 3 AM.
The economics of local AI are simple: if you're spending more than $50/month on AI API calls, a used GPU pays for itself in months. And you get to keep your data.
#AI #AMD #GPU #LocalAI #HomeLab #MachineLearning #ROCm #LLM