Qwen3-30B-A3B is the largest and most powerful model in Alibaba Cloud’s Qwen3 series – designed for maximum speech understanding performance, high inference quality and complex enterprise-level applications. With 30 billion parameters in the A3B architecture, the model combines state-of-the-art training methods, advanced Instruct capabilities and a very large context window.
Qwen3-30B-A3B is fully open-source, published under the Apache 2.0 license and is ideal for production-ready AI applications with the highest demands on quality, scalability and control.
Qwen3-30B-A3B (part of the Qwen3 model family)
Qwen Team (Alibaba Group)
April 29, 2025
Mixture-of-Experts (MoE) Causal Language Model based on Transformer.
Total: 30.5 billion, activated per token: 3.3 billion, without embedding: 29.9 billion
Qwen2 Tokenizer (Tiktoken-based), vocabulary size: 151.936. Compatible with current Hugging Face transformers library (chat template available for Instruct/Chat variants).
48 Transformer layer
32 query headers, 4 key/value headers (Grouped-Query Attention - GQA).
Total number of experts: 128, activated experts per token: 8
Native: 32,768 tokens (32K), with YaRN scaling: up to 131,072 tokens
The Qwen3 series includes various model sizes, both dense and MoE models:
Available variants include basic models (“Base”), instruction-fine-tuned models (“Instruct”) and chat models (“Chat”).
We would be happy to advise you individually on which AI model suits your requirements. Arrange a no-obligation initial consultation with our AI experts and exploit the full potential of AI for your project!
Qwen3-30B-A3B, the flagship model of the Qwen3 series, has been trained on a comprehensive, curated dataset of over 3.5 trillion tokens. The training data comes from a high-quality mix of publicly available sources, including web texts, program code, books and scientific papers. For maximum robustness, security and model quality, a multi-stage data preparation process was used, which systematically filtered out irrelevant or risky content and merged the data in a targeted, weighted manner.
In post-training, the model was first adapted to a wide range of instructional data with the help of supervised fine-tuning (SFT). This was followed by targeted refinement using reinforcement learning from human feedback (RLHF) – including direct preference optimization (DPO)– in order to adapt the model even more closely to human preferences, comprehensibility and usability in real-life applications.
Is Qwen3-30B-A3B the right AI model for your individual application? We will be happy to advise you comprehensively and personally.
Significantly improved reasoning skills.
Excellent adaptation to human preferences for natural conversations.
Strong skills in agentic use and tool calling.
Very good multilingual support (over 100 languages).
Ability to process long contexts with YaRN (up to 131K tokens).
“Thinking Mode” for improved performance in complex tasks.
More efficient inference compared to dense models with a similar total number of parameters due to the MoE architecture (only 3.3B parameters active).
Fully open source under Apache 2.0 license (both code and model weights), allowing commercial use.
Part of a comprehensive family of models (Qwen3).
Still requires significant hardware resources, although it is more efficient than a dense 30B model.
Complexity of MoE architecture can complicate inference optimization in some frameworks.
Standard disadvantages of LLMs: potential for hallucinations, bias and lack of transparency.
Performance on shorter texts can potentially be affected if static YaRN is enabled for long contexts.
Qwen3-30B-A3B combines the highest level of speech understanding with full control through open source licensing. Whether for assistance systems, business-critical AI solutions or specialized research – we support you in the selection, integration and hosting of this powerful model. Fully managed in our German GPU Cloud on request.
Yes, with strong quantization (e.g. via llama.cpp GGUF) and sufficient RAM (at least 32-64GB recommended) CPU inference is possible, but speed will likely be limited for interactive applications. GPU acceleration is recommended for better performance.
For FP16 inference approx. 60-70 GB. With 4-bit quantization, the requirement can be reduced to approx. 15-20 GB VRAM, which enables operation on high-end consumer GPUs. Exact figures depend on the configuration.
Yes, both the code and the model weights of Qwen3-30B-A3B are published under the Apache 2.0 license, which allows commercial use.
The model natively supports 32K tokens. For longer contexts (up to 131K), the YaRN scaling method can be enabled in compatible frameworks. Please note the information on potential performance degradation for shorter texts when using static YaRN.
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