Running Local LLMs for Coding: No API Keys, Full Control

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.

Why Local Actually Makes Sense Now

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:

• No API costs — Run it once, use it forever
• Privacy — Your code stays on your machine (huge for work projects)
• Offline capability — Airport wifi? No problem
• Speed — Local inference beats network latency

The downside: You need about 8GB of VRAM minimum. 16GB is comfortable. If you're on older hardware, this won't work.

The Setup I Actually Use

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:

1. Ollama — Model management and server (ollama.ai)
2. Continue.dev — VS Code extension that hooks into Ollama
3. Mistral 7B — The model (good balance of speed and smarts)

Installation takes 10 minutes:

# Install Ollama
brew install ollama  # or download from ollama.ai

# Start the server
ollama serve

# In another terminal, pull a model
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.

Real Examples That Saved Me Time

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.

Where It Falls Short

This isn't a magic tool. Mistral 7B (and other 7B models) genuinely struggle with:

• Complex architectural decisions
• Large codebase context (it forgets things beyond ~4K tokens)
• Highly novel problems (it defaults to common patterns)

For these, I still use Claude for serious thinking. Local models are for coding speed, not problem solving.

Performance Reality

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.

Worth It?

If you're:

• A solo developer or small team (no shared GPU infra)
• Tired of API costs (even $10/month adds up)
• Working on sensitive code (can't upload to third-party)
• Want to experiment with different models

Then absolutely. Set aside an hour, get it running, see if it fits your workflow.

If you're:

• Using enterprise AI platforms already
• Need production-grade model support
• Working in a team with shared compute

Then stick with what you have. Local models are a productivity tool, not a replacement for serious infrastructure.

Next Steps

1. Install Ollama if your hardware supports it
2. Try it with a small coding task first
3. If the speed is there, integrate it into your editor
4. Dial in which models work for your actual coding style

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.