A practical guide to running LLMs on your own hardware in early 2026. Covers hardware requirements by VRAM tier (8 GB to 48 GB+), tool comparison (Ollama, LM Studio, Jan, llama.cpp, vLLM), quantization formats (GGUF, Q4_K_M explained), recommended models (Qwen 3, Gemma 3, Llama 4), and workflow integration via OpenAI-compatible
Table of contents
Why Bother Running LLMs Locally?What Hardware Do You Actually Need?The Tools: Ollama, LM Studio, Jan, and BeyondChoosing a Model and Understanding QuantizationAdding a UI and Integrating Into Your WorkflowLimitations and When to Use Cloud InsteadSort: