I want to use AI for coding but my company forbids cloud-based assistants. Could local AI provide enough logic for complex refactoring? I'm worried that without the massive clusters behind things like ChatGPT, the code quality will suffer.
3 answers
For refactoring and boilerplate, it's actually excellent. Models like CodeLlama or StarCoder are specifically trained for this and can run quite well on a workstation with 32GB of RAM. You might not get the broad "architectural" advice that a massive model offers, but for local file analysis and syntax suggestions, it’s a game changer. The key is to use a tool that indexes your local codebase so the model has context. This way, you get the benefits of an AI pair programmer without your proprietary code ever leaving your machine, which satisfies even the strictest IT security policies.
Timothy here. How do these local coding models compare in terms of latency when you are working on a massive enterprise-scale repository?
I've switched to a local setup for my private projects and I haven't looked back. The lack of lag is the best part of the whole experience.
Karen is right. The immediate feedback loop of a local model is a huge boost to developer productivity that often gets overlooked in these discussions.
Timothy, latency depends entirely on your GPU's VRAM. If the model fits into your video memory, it's nearly instantaneous—often faster than waiting for a cloud API response. However, if it spills over into system RAM, it becomes sluggish. For large repos, you need to be smart about what you include in the context window to keep the speed usable during active development.