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

> Source: <https://dev.to/learnairesource/running-local-llms-for-coding-no-api-keys-full-control-48cj>
> Published: 2026-06-24 15:00:32+00:00

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:

```
# 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.

**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.