Running Local LLMs for Development

Running LLMs locally has become practical in 2026 thanks to smaller, more efficient models and better tooling. For developers who cannot send proprietary code to cloud APIs, local models offer a compelling alternative.

Why Local LLMs

Three reasons drive local LLM adoption for development: privacy (your code never leaves your machine), cost (no per-token charges), and latency (no network round-trips). For tasks like code completion, documentation generation, and test writing, local models now perform well enough for daily use.

Hardware Requirements

For coding tasks, you want at least 16GB of RAM and a GPU with 8GB+ VRAM. Apple Silicon Macs are excellent for local inference — an M2 Pro with 32GB unified memory runs 13B parameter models comfortably. On Linux, an NVIDIA RTX 4070 or better handles 7B-13B models with good throughput. You can run 7B models on CPU alone, but expect 5-10x slower generation.

Setting Up Ollama

Ollama is the easiest way to run local models. Install it, then pull a coding model: ollama pull codellama:13b or ollama pull deepseek-coder:6.7b. Ollama exposes an OpenAI-compatible API on localhost, so any tool that supports the OpenAI API can point to your local model instead.

Integrating with Your Editor

Continue (the open-source AI code assistant) supports Ollama as a backend. Configure it to use your local model for tab completion and chat. VS Code extensions like llm-vscode also support local backends. For terminal-based workflows, use aider or open-interpreter with the local model API endpoint.

Model Selection for Coding

DeepSeek Coder V2 is the current leader for local code generation, offering GPT-4-class performance at 7B-33B parameter sizes. CodeLlama remains solid for completion tasks. Mistral Codestral is excellent for instruction following. For each model, test it on your actual codebase before committing — performance varies significantly across languages and frameworks.