Run a Local LLM for Code Review: Setup and Privacy Checks
A practical guide to running a language model on your own hardware and checking which parts of your editor or workflow may still use network services.
Step-by-Step Instructions
Choose a model that fits your hardware
Check available RAM, GPU memory, storage, and the model's context requirements before downloading weights. Start with a smaller coding model and a test repository; model size and quantization affect speed and output quality.
"ollama run deepseek-coder-v2:16b"
Point your editor at the local inference endpoint
Install the local runtime from its official documentation, download a compatible model, and confirm that it responds on the local endpoint. Configure your editor or script to use that endpoint, and verify the selected model is local rather than a cloud-hosted variant.
Verify network behavior before using private code
Local inference can keep prompt processing on your machine, but it does not automatically make every surrounding application private. Review editor telemetry, extensions, update checks, cloud fallbacks, plugins, and external tools. Use a network monitor or firewall test to confirm what leaves the device before processing proprietary code.
Official documentation & further reading
These first-party resources help verify product workflows, eligibility, privacy terms, and plan details. Providers can change features and limits, so check the current source before making a purchase or production decision.
