The 24GB AI Lab: A Survival Guide to Full-Stack Local AI on Consumer Hardware

We’ve all been there: You see a viral post about a new AI model, you try to run a fine-tune locally, and your terminal rewards you with a wall of red text and a CUDA Out of Memory error. If you’re running a mid-range, multi-GPU setup—specifically a dual-GPU rig like the NVIDIA RTX 3060 (12GB each)—you aren't just a hobbyist; you’re an orchestrator. You have 24GB of total VRAM, but because it’s physically split across two cards, the default settings of almost every AI tool will crash your system. After months of trial and error in a Dockerized Windows environment, I’ve developed a "Zero-Crash Pipeline." This is the exact blueprint for taking a model from a raw fine-tune in Unsloth to an agentic reality using Ollama, OpenClaw, and ComfyUI. 1. The Foundation: Docker & The "Windows Handshake" Running your ML environment in Docker (using the Unsloth image) keeps your Windows host clean, but Docker needs strict instructions on how to handle memory across two GPUs. Before you even load a mode