There are three main ways to deploy Qwen3-Coder locally:
- Ollama Program: Ollama version 0.6.6 and above is required, run the
ollama servepostponedollama run qwen3:8bLoading the model. The model can be loaded via the/set parameter num_ctx 40960Adjusting the context length, the API address ishttp://localhost:11434/v1/, suitable for rapid prototyping. - llama.cpp programThe startup command includes several optimization parameters such as
--temp 0.6 --top-k 20 -c 40960etc., which maximizes the use of local GPU resources (NVIDIA CUDA or AMD ROCm), and service port 8080 by default. - Transformers Native Deployment: loaded directly through the HuggingFace repository using the
AutoModelForCausalLMinterface, supports full precision and quantized (4bit/8bit) loading. At least 16GB of video memory is required to run the 7B model smoothly.
Recommended configuration: NVIDIA RTX 3090 or above graphics card, Ubuntu 22.04 system, Python 3.10 environment. It is recommended to download the pre-quantized model from ModelScope to reduce the hardware pressure for the first deployment.
This answer comes from the articleQwen3-Coder: open source code generation and intelligent programming assistantThe
May not be reproduced without permission:AI productivity tools " How to deploy a local development environment for Qwen3-Coder?
English 
简体中文 
日本語 
Deutsch 
Português do Brasil