技术规格

系统要求

不同量化方法和上下文大小的显存要求

Qwen3 Coder 480B A35B

Qwen3 Coder 480B A35B is Alibaba's advanced agentic AI coding model, developed for high-performance software development and autonomous coding workflows. This model is engineered to excel in tasks such as code generation, managing complex multi-turn programming workflows, and debugging entire codebases. It is designed to facilitate autonomous software engineering, enabling comprehensive repository analysis, cross-file reasoning, and automated pull request workflows. The model also supports integration with various developer tools and platforms, including its own open-sourced command-line interface, Qwen Code, which is adapted for customized prompts and function calling protocols.

Architecturally, Qwen3 Coder 480B A35B is a Mixture-of-Experts (MoE) model. It comprises a total of 480 billion parameters, with 35 billion active parameters utilized per inference query. This sparse activation strategy, involving 8 active experts out of 160 total experts, allows for efficient computation while maintaining high performance. The model features 62 layers and employs a Grouped Query Attention (GQA) mechanism with 96 query heads and 8 key-value heads. The core is a decoder-only transformer, optimized for MoE and enhanced with RoPE (Rotary Position Embedding) for handling extended context lengths.

The model's performance characteristics are geared towards real-world software engineering tasks. It demonstrates capabilities in advanced agentic coding, browser automation, and tool usage, supporting a wide range of programming and markup languages, including Python, JavaScript, Java, C++, Go, and Rust. Qwen3 Coder 480B A35B's training involved 7.5 trillion tokens with a 70% code ratio, balancing coding, general, and mathematical abilities. Post-training incorporates long-horizon reinforcement learning (Agent RL) to enable the model to solve complex problems through multi-step interactions with external tools.