Local AI for Software Developers: Code Completion and Review with Private Models

Software developers have good reasons to keep source code private during AI-assisted development. Sending proprietary code to cloud APIs raises legal, security, and compliance questions that local models sidestep entirely. With modern local LLMs, you can get high-quality code completion, review, and documentation — all running on your own hardware.

For an introduction to setting up local models, start with How to Run Llama 3 Locally with Ollama.

Choosing a code-focused local model

Small models struggle with complex code reasoning, but recent 7B and 8B parameter models fine-tuned on code perform surprisingly well. DeepSeek-Coder, CodeQwen, and CodeLlama variants in 4-bit quantisation run comfortably on consumer GPUs and handle most practical code tasks.

For model recommendations, see Best Local AI Models for Beginners.

Code completion and inline suggestions

While full IDE integration is not yet mature for local models, you can build a practical workflow using the Ollama API or TabbyAPI from within your editor. Set up a keybinding that sends the current file context to a local model and inserts the completion.

The key to useful completions is sending enough context: the current function, imports, and the surrounding module structure. Local models with 8K or 16K context windows handle this well.

Automated code review

Local models excel at focused code review tasks. Create a script that extracts a diff or changed function, sends it to the model with a review prompt, and returns suggestions:

```bash

git diff --cached | ollama run codellama "Review this diff for bugs, style issues, and security problems"

```

Run this as a pre-commit hook or as part of your CI pipeline. The review stays on your machine and never touches external servers.

Documentation generation

Generating documentation from code is a natural fit for local LLMs. Send a function or module with a prompt like "Write JSDoc for this function" or "Generate a README section explaining this API."

The model produces documentation that you can edit and refine. This is faster than writing from scratch and catches edge cases you might forget to document.

Debugging and error analysis

When you hit a confusing error, paste the stack trace and relevant code into a local model. Models trained on code understand common error patterns and can suggest fixes, root causes, or debugging approaches.

Combine this with the terminal tool in any local AI interface for an interactive debugging session.

Workflow integration

Use n8n or a simple shell script to chain these tasks together: detect a code change, run a review, generate documentation, and flag issues. All context stays local.

For automation patterns, read Build Your Own AI Assistant with n8n.

Conclusion

Local AI for software development is practical today for code review, documentation, and debugging. The models are good enough for meaningful assistance, and the privacy guarantee makes it the right choice for proprietary codebases.

FAQ

Which local model is best for code?

DeepSeek-Coder 6.7B and CodeQwen 7B perform well on code tasks at sizes that run on consumer hardware.

Can local models replace GitHub Copilot?

Not entirely for inline completion, but they are excellent for review, documentation, and debugging where Copilot-style suggestions are less useful.

Do I need a powerful GPU for code models?

A 7B model at 4-bit quantisation needs about 6GB VRAM. Many developers run these on RTX 3060 or RTX 4060 class GPUs.