While everyone else is paying $20/month for cloud APIs, privacy-conscious developers are running Qwen 2.5 Coder locally. Alibaba’s open-weights models have caught up to GPT-4o in coding benchmarks (like SWE-bench), making them the default choice for air-gapped environments and local agentic frameworks.
Here is the no-nonsense cheatsheet for running Qwen Coder on your own silicon in 2026.
Running Qwen via Ollama
Ollama is the easiest way to get Qwen running on macOS, Linux, or WSL.
# Pull and run the 7B model (Good for M1/M2 Macs with 16GB RAM)
ollama run qwen2.5-coder:7b
# Pull the massive 32B model (Requires 32GB+ RAM or a dedicated GPU)
ollama run qwen2.5-coder:32b
# Start the REST API server in the background
ollama serveThe Scenario: You’re working on a proprietary defense contract. Your NDA strictly forbids pasting code into ChatGPT or Claude. You pull
qwen2.5-coder:32bvia Ollama. It runs entirely on your local GPU. You can now use a full-powered coding agent without violating your contract or sending a single packet over the network.
Integrating Qwen with the Vercel AI SDK
You don’t need OpenAI to build an agent. You can use the Vercel AI SDK with a local Ollama instance running Qwen.
// npm install ai ollama-ai-provider
import { generateText } from 'ai';
import { createOllama } from 'ollama-ai-provider';
// Connect to your local Ollama instance
const ollama = createOllama({
baseURL: 'http://localhost:11434/api',
});
const response = await generateText({
model: ollama('qwen2.5-coder:32b'),
prompt: 'Write a quicksort algorithm in Rust.',
});
console.log(response.text);IDE Integration (Continue & Cursor)
You can point your favorite AI code editors to your local Qwen model to get free, unlimited autocomplete.
In Continue.dev:
Add this to your config.json:
{
"models": [
{
"title": "Local Qwen Coder",
"provider": "ollama",
"model": "qwen2.5-coder:32b",
"apiBase": "http://localhost:11434"
}
],
"tabAutocompleteModel": {
"title": "Qwen Autocomplete",
"provider": "ollama",
"model": "qwen2.5-coder:7b" // Use the smaller model for faster Tab predictions
}
}The Scenario: You’re working on an airplane with no Wi-Fi. You open VS Code with the Continue extension. Because you mapped
tabAutocompleteModelto your localqwen2.5-coder:7b, you still get full, context-aware code completions while flying at 30,000 feet.
Prompting for Context
Qwen 2.5 Coder supports a 128k context window, but running that locally takes massive VRAM. Be surgical with your prompts.
The “Strict Code” Prompt: If Qwen keeps generating markdown explanations when you only want raw code, use this system prompt:
“You are an expert programmer. You MUST output ONLY raw, executable code. Do not use Markdown formatting (e.g., ```). Do not include greetings or explanations. Begin immediately with the code.”
Hardware Requirements Reference
Don’t crash your machine trying to run a model that’s too big.
- 1.5B Model: Runs on anything. Great for basic autocomplete. (Requires ~2GB RAM)
- 7B Model: The sweet spot for M-series Macs and standard developer laptops. (Requires ~8GB RAM)
- 32B Model: Production-grade reasoning. (Requires ~24GB+ VRAM/Unified Memory)
Found this useful? Check out our Docker Cheatsheet to learn how to containerize your local AI agents.