How to Set Up a Local LLM for Developer Workflows Verify your hardware meets minimum specs (8–16 GB RAM/VRAM for 7B models at Q4_K_M quantization). Install a runtime — Ollama via a single shell command or LM Studio via its GUI installer. Pull a recommended model for your use case (e.g., ollama pull qwen3:8b ). Confirm the local OpenAI-compatible API is running on localhost. Point your IDE extension or application code at the local endpoint by updating the base URL. Benchmark response quality and token-per-second speed against your cloud baseline. Configure a Modelfile or preset with a project-specific system prompt and context window size. Document the setup for your team using a Dockerfile or install script. Running local LLMs has shifted from a hobbyist pursuit to a practical engineering decision. This guide covers hardware requirements, tool installation, API integration, IDE setup, performance benchmarks, model recommendations, and the pitfalls that still trip people up.…