Qwen-3: Explore the Next-Generation Open Source Large Model

DeepSeek V3 outperforms both open-source and closed-source AI models in programming competitions, particularly excelling in Codeforces competitions and Aider Polyglot tests.

Possesses 671 billion parameters and trained on 14.8 trillion tokens, 1.6 times the scale of Meta's Llama 3.1 405B.

Completed training in just two months using Nvidia H800 GPUs, with a development cost of only $5.5 million.

Efficient Inference Frameworks (vLLM & SGLang)

Recommended for high-performance deployment using vLLM (>=0.8.4) or SGLang (>=0.4.6.post1), supporting long context and Hybrid Thinking Mode.

• High throughput
• Low latency
• Supports Hybrid Thinking Mode
• Compatible with OpenAI API

Convenient Local Deployment

Easily run Qwen-3 models locally using tools like Ollama, LMStudio, MLX, llama.cpp, KTransformers, etc.

• Quick start
• Cross-platform support (CPU/GPU)
• Active community
• Support for various quantization formats

Cloud API Services

Directly call the Qwen-3 API via Alibaba Cloud Bailian, DashScope, or together.ai without self-deployment.

• Out-of-the-box
• Pay-as-you-go
• Global access
• Enterprise-level support

Model Platforms & Quantization Formats

Model weights are available on Hugging Face, ModelScope, Kaggle. Supports quantization formats like GGUF, AWQ, AutoGPTQ to reduce resource requirements.

• Multi-platform access
• Apache 2.0 License
• Supports Int4/Int8 quantization
• Suitable for consumer hardware

What makes Qwen-3 unique?

Qwen-3 offers various model sizes from 0.6B to 235B (MoE), open-sourced under Apache 2.0. Key innovations include the Hybrid Thinking Mode (intelligently switching thought depth), unified multimodal processing capabilities, and broad support for 119 languages.

How can I access or use Qwen-3?

You can download model weights from Hugging Face, ModelScope, or Kaggle for local deployment (tools like vLLM, SGLang, Ollama are recommended). You can also call API services via Alibaba Cloud Bailian, DashScope, together.ai, etc., or experience it directly on the Qwen Chat website/app.

What tasks does Qwen-3 excel at?

Qwen-3 demonstrates leading performance in coding, mathematics, and general capability benchmarks, surpassing models like Llama3.1-405B. Its multilingual abilities, long context processing, and Agent functionalities (with MCP protocol) are also very strong.

What is Hybrid Thinking Mode?

This is an innovative feature of Qwen-3. The model can automatically or manually switch between a 'thinking mode' for deep reasoning and a 'non-thinking mode' for quick responses, based on task complexity, to balance effectiveness and efficiency.

How many languages does Qwen-3 support?

Qwen-3 supports up to 119 languages and dialects, significantly enhancing cross-lingual understanding and generation capabilities through large-scale multilingual pre-training data (nearly 36T tokens).

What are the hardware requirements for running Qwen-3?

Requirements depend on the model size. Smaller models (e.g., 0.6B, 1.7B) can run on consumer hardware, especially with Int4/Int8 quantization (like GGUF). Larger models (e.g., 32B, 235B) require more powerful GPU support. It's recommended to check the specific model's documentation and quantization options.

Is Qwen-3 available for commercial use?

Yes, all models in the Qwen-3 series are released under the Apache 2.0 license, allowing for both commercial and research use.

What is the context window size of Qwen-3?

Depending on the model size, Qwen-3 dense models support context lengths of 32K (0.6B-4B) or 128K (8B-32B) tokens. MoE models also support long context (check the model card for specific sizes).

Which deployment frameworks/tools does Qwen-3 support?

vLLM (>=0.8.4) and SGLang (>=0.4.6.post1) are recommended for efficient deployment. For local execution, you can use Ollama, LMStudio, llama.cpp, MLX-LM, KTransformers, etc. It is also compatible with the Hugging Face Transformers library.