You've probably noticed the code completion tools getting slower and more rate-limited. You've also probably gotten tired of explaining your entire codebase to an API that costs money per token. What if I told you could run your own LLM locally and get genuinely faster completions?
I spent the last month setting up a local LLM workflow, and yeah, it's better than outsourcing to APIs. Here's what I actually use.
Six months ago, local models were slow. Now? Not so much. Ollama + a decent GPU gets you sub-second completions for code tasks. That's faster than waiting for an API call half the time.
The benefits are real:
The downside: You need about 8GB of VRAM minimum. 16GB is comfortable. If you're on older hardware, this won't work.
Hardware: MacBook Pro 16" with M3 Max (36GB unified memory). On Linux? Similar story — need a decent GPU or CPU with enough cores.
Tool stack:
Installation takes 10 minutes:
brew install ollama # or download from ollama.ai
ollama serve
ollama pull mistral
That's it. Ollama runs on localhost:11434
by default.
For Continue, I grabbed the VS Code extension and configured it:
{
"models": [
{
"title": "Mistral 7B Local",
"model": "mistral",
"apiBase": "http://localhost:11434/api",
"provider": "ollama"
}
]
}
Now I use Ctrl+K (or Cmd+K on Mac) to trigger inline code generation. It works. Actually works.
Example 1: Boilerplate Generation
I needed a Redux reducer with a few specific actions. Mistral nailed it on the first try — structured correctly, no hallucinations, just gave me what I asked for. Saved 5 minutes of manual typing.
Example 2: Bug Diagnosis
Pasted a stack trace, asked what was happening. Got a correct answer with a fix. Not a wild guess — the actual issue was a missing async/await in a parent function. Saved me 20 minutes of debugging.
Example 3: Test Writing
Asked it to generate tests for a utility function. Generated decent test cases using Jest. Needed minor tweaks but 80% complete. Normal.
This isn't a magic tool. Mistral 7B (and other 7B models) genuinely struggle with:
For these, I still use Claude for serious thinking. Local models are for coding speed, not problem solving.
On my M3 Max, inference takes 0.5-2 seconds for code completions. That's real-world, not benchmark. Sometimes slower, sometimes faster depending on what's running.
Compare that to waiting 3-5 seconds for an API request to round-trip, and the local option wins.
If you're:
Then absolutely. Set aside an hour, get it running, see if it fits your workflow.
If you're:
Then stick with what you have. Local models are a productivity tool, not a replacement for serious infrastructure.
Also — if you're building your own AI tooling, stay in the loop with ** LearnAI Weekly** for deeper dives on local models, open-source tools, and what's actually worth your time.
The future of coding tools is personal. Control yours.