# Ollama Coding Assistant: Complete Guide (2026)
If you’re tired of sending your code to cloud services every time you need AI assistance, Ollama gives you a local alternative that runs entirely on your machine. In this guide, I’ll walk you through setting up Ollama as a coding assistant, show you how to integrate it with your editor, and help you understand when local AI makes sense versus when you should still reach for GPT-4 or Claude.
This isn’t about replacing cloud AI—it’s about having a fast, private option for everyday coding tasks without latency or API costs eating into your workflow.
## What Ollama Actually Is
Ollama is an open-source runtime for running large language models locally. It bundles model weights into a runnable format and provides a simple API for interaction. Think of it as a self-hosted API that speaks the same language as OpenAI’s API, making it easy to swap in your scripts.
The key difference from cloud AI: everything happens on your hardware. No data leaves your machine. No per-token costs. No rate limits beyond what your GPU can handle.
For coding tasks specifically, Ollama supports models like CodeLlama, Mistral, and Llama 3—models trained on code datasets that can debug, refactor, and explain your code.
**What you need to know upfront:**
– Runs on macOS, Linux, and Windows (via WSL2)
– Requires sufficient RAM (8GB minimum for smaller models, 16GB+ for code-capable models)
– GPU acceleration via Metal on Mac, CUDA on NVIDIA, or CPU-only as fallback
## Installing Ollama
Installation is straightforward. On macOS and Linux:
“`bash
curl -fsSL https://ollama.com/install.sh | sh
“`
On Windows, download the installer from ollama.com or use WSL2 with the Linux install script.
Verify the installation:
“`bash
ollama –version
“`
That’s it. No Docker, no complex setup. The binary pulls models on demand when you first run them.
## Pulling and Running Models
Ollama manages models through a simple command interface. To get a code-capable model:
“`bash
ollama pull codellama
“`
Or for the newer Llama 3 variant with strong coding capabilities:
“`bash
ollama pull llama3
“`
List available models:
“`bash
ollama list
“`
Run the model interactively:
“`bash
ollama run llama3
“`
This opens an interactive chat session in your terminal. Type your prompt, get your response. Exit with `/bye`.
But you want this for coding, not terminal chat. Let’s expose it as an API.
## Using Ollama’s API
Ollama runs a local API server on port 11434 by default. Here’s a basic request:
“`bash
curl http://localhost:11434/api/generate -d ‘{
“model”: “llama3”,
“prompt”: “Explain what this Python function does: def foo(x): return [i for i in range(x) if i % 2 == 0]”,
“stream”: false
}’
“`
The response is JSON with the model’s output. For streaming responses (more natural for coding assistance):
“`bash
curl http://localhost:11434/api/generate -d ‘{
“model”: “llama3”,
“prompt”: “Write a Python function to find the longest palindrome substring”,
“stream”: true
}’
“`
This API compatibility with OpenAI’s format means you can point existing tools at your local endpoint with minimal configuration changes.
## Integrating with VS Code
The practical value comes from having this in your editor. Here’s how to wire Ollama into VS Code.
**Option 1: Using the continue extension**
Continue (continue.dev) is a VS Code extension that supports custom AI backends. Add this to your `config.json`:
“`json
{
“models”: [
{
“model”: “llama3”,
“api_base”: “http://localhost:11434”,
“provider”: “openai”
}
]
}
“`
Now you get inline code completion, chat, and refactoring powered by your local Ollama instance.
**Option 2: Using Ollama’s built-in completion (simpler)**
For basic autocompletion without additional extensions, you can set up a keybinding that sends selected code to Ollama:
1. Install the “Shell Commands” extension or create a custom keybinding in `keybindings.json`:
“`json
{
“key”: “cmd+shift+o”,
“command”: “workbench.action.terminal.sendSequence”,
“args”: {
“text”: “curl -s http://localhost:11434/api/generate -d ‘{\”model\”: \”llama3\”, \”prompt\”: \”Complete this code: ” + “${selectedText}” + “\”, \”stream\”: false}’ | jq -r ‘.response’\n”
}
}
“`
That’s a rough example—the real implementation usually involves a small extension or script. The point is: your local AI is accessible from within your editor, and the integration path is straightforward.
## Practical Examples
Let’s look at real coding tasks where Ollama performs well.
**Debugging code:**
“`bash
curl http://localhost:11434/api/generate -d ‘{
“model”: “llama3”,
“prompt”: “Debug this JavaScript: function getUser(id) { return users.find(u => u.id === id) } returns undefined for valid ids”,
“stream”: false
}’
“`
The model will likely identify that `users` is undefined in scope or that the function needs error handling. It’s not magic, but it often spots obvious issues quickly.
**Explaining code:**
“`bash
curl http://localhost:11434/api/generate -d ‘{
“model”: “llama3”,
“prompt”: “Explain this SQL query in plain English: SELECT u.name, COUNT(o.id) FROM users u LEFT JOIN orders o ON u.id = o.user_id GROUP BY u.id HAVING COUNT(o.id) > 5”,
“stream”: false
}’
“`
**Refactoring:**
“`bash
curl http://localhost:11434/api/generate -d ‘{
“model”: “llama3”,
“prompt”: “Refactor this Python to use list comprehension: result = []\nfor i in range(10):\n if i % 2 == 0:\n result.append(i * 2)”,
“stream”: false
}’
“`
These work. They’re fast. They don’t cost anything per request. For quick, iterative tasks, this is genuinely useful.
## Limitations You Need to Accept
Ollama isn’t a replacement for GPT-4 or Claude in all scenarios. Here’s where it falls short:
**Reasoning quality:** Local models are smaller to fit on consumer hardware. CodeLlama 7B or Llama3 8B don’t reason as deeply as the largest cloud models. Complex architectural decisions or multi-file refactoring often produce worse results than Claude.
**Speed:** On CPU only, expect 5-15 seconds per response. With a decent GPU, you might get 2-5 seconds. Cloud AI is often faster for quick queries.
**Context window:** Most Ollama models max out at 4K-8K context. Large codebase analysis or long document processing won’t work well.
**No tool use:** Cloud models can browse the web, run commands, use tools. Ollama models are text-only outputs.
Use Ollama for: quick code questions, simple refactoring, code explanation, learning new syntax. Stick with cloud AI for: complex debugging across files, architectural decisions, or anything requiring tool use.
## Key Takeaways
– Ollama runs local LLMs with an OpenAI-compatible API, keeping your code data on your machine
– Installation is a single curl command; pulling and running models takes minutes
– CodeLlama and Llama3 are the primary code-capable models available
– VS Code integration via the continue extension or custom keybindings gives you in-editor access
– Local AI trades reasoning quality for privacy, speed, and zero per-request cost
## Next Steps
1. Install Ollama and pull `llama3` or `codellama` with `ollama pull llama3`
2. Test the API with a simple curl request to verify it’s running
3. Install the continue extension in VS Code and configure it to point to your local endpoint
4. Use it for your next small coding task—debugging a function or explaining a snippet
5. If it’s too slow, check your GPU acceleration (CUDA on NVIDIA, Metal on Mac M-series)
Ollama won’t replace your cloud AI assistant, but it fills a gap for quick, private, cost-free coding assistance. Run it in the background, forget about it, and have it ready when you need it.
