Qwen3-Coder-480B: The Most Powerful Open-Source Coding AI

🚀 Overview

Qwen3-Coder-480B is a massive 480 billion parameter Mixture of Experts (MoE) model, specifically designed for advanced code generation, understanding, and software development tasks. With 160 experts and 8 active per token (35B active parameters), it delivers unparalleled coding capabilities while maintaining computational efficiency.

🎯 Key Features

• 480B total parameters with 35B active (MoE architecture)
• 262K context length (expandable to 1M with YaRN)
• 100+ programming languages supported
• State-of-the-art performance on coding benchmarks
• Multiple quantizations from 163GB to 368GB

📊 Benchmark Results

• HumanEval: 89.3% pass@1
• MBPP: 78.2% pass@1
  
• CodeContests: 42.7% success rate
• MultiPL-E: Leading scores across 18 languages

🏷️ Available Versions

💻 Quick Start

# Recommended version (Q4_K_M)
ollama run richardyoung/qwen3-coder:q4-k-m "Write a Python web server with async support"

# Smallest version for testing (Q2_K)
ollama run richardyoung/qwen3-coder:q2-k "Explain the quicksort algorithm"

# High quality version (Q6_K)
ollama run richardyoung/qwen3-coder:q6-k "Implement a red-black tree in Rust"

🛠️ Example Use Cases

Code Generation

ollama run richardyoung/qwen3-coder:q4-k-m "Create a React component for infinite scrolling with virtualization"

Code Review

ollama run richardyoung/qwen3-coder:q4-k-m "Review this code for security vulnerabilities: [paste your code]"

Algorithm Implementation

ollama run richardyoung/qwen3-coder:q4-k-m "Implement Dijkstra's algorithm in Python with detailed comments"

Code Translation

ollama run richardyoung/qwen3-coder:q4-k-m "Convert this JavaScript function to Rust: [paste function]"

🔧 Advanced Configuration

Custom Parameters

ollama run richardyoung/qwen3-coder:q4-k-m \
  --temperature 0.7 \
  --top-p 0.9 \
  --top-k 20 \
  --repeat-penalty 1.05 \
  "Your prompt here"

Extended Context

# For larger codebases (up to 32K tokens)
ollama run richardyoung/qwen3-coder:q4-k-m \
  --num-ctx 32768 \
  "Analyze this codebase and suggest improvements"

📋 System Requirements

Minimum Requirements

• RAM: 256GB (for Q2_K with partial GPU offload)
• GPU: 2x RTX 4090 or equivalent
• Storage: 400GB free space

Recommended Setup

• RAM: 512GB
• GPU: 4x A100 80GB or 8x RTX 4090
• Storage: 1TB NVMe SSD

🌟 What Makes This Model Special

1. Specialized Training: 5.5T tokens of high-quality code and technical content
2. MoE Efficiency: Only 35B active parameters despite 480B total size
3. Language Coverage: Exceptional performance across 100+ programming languages
4. Context Understanding: Native 262K context for large codebases
5. Production Ready: Extensively tested on real-world coding tasks

🤝 Community & Support

• Model Card: Based on Qwen/Qwen3-Coder-480B-A35B-Instruct
• Quantization: Created using llama.cpp
• Issues: Report via Ollama community forums
• Updates: Follow for new quantizations and improvements

📝 License

This model follows the Qwen model license. Please refer to the original model repository for detailed licensing information.

🙏 Acknowledgments

• Qwen Team for creating this exceptional model
• llama.cpp community for quantization tools
• Ollama for making large models accessible

Note: Due to the model’s size, downloads may take considerable time. Ensure stable internet connection and sufficient storage before pulling.