Operating System
Windows Self-Hosting
Run local LLMs on Windows with WSL2, native CUDA, and tools like LM Studio and Ollama.
WindowsWSL2CUDAOllamaself-hosting
Recommended approach
- WSL2 with Ubuntu for the cleanest Python/PyTorch/CUDA experience
- Native Windows for GUI tools like LM Studio, KoboldCPP, or local Stable Diffusion suites
Minimal configuration
- GPU: NVIDIA RTX 3060 12 GB
- RAM: 32 GB
- Storage: 512 GB NVMe SSD
- OS: Windows 11 23H2 with WSL2 Ubuntu 24.04
Recommended configuration
- GPU: NVIDIA RTX 4090 24 GB
- RAM: 64 GB
- Storage: 2 TB NVMe SSD
- OS: Windows 11 23H2
- Stack: WSL2 + Ollama or LM Studio
Notes
AMD ROCm on Windows is limited; NVIDIA is currently the most reliable path. Some Apple Silicon users prefer macOS for native MLX performance.