Models / Qwen
Chat

Qwen3 30B A3B

30.5B-parameter Mixture-of-Experts chat model with 3.3B activated parameters optimized for conversational AI and reasoning tasks across 119 languages.

About model

Qwen3-30B-A3B is a large language model offering advanced reasoning, instruction-following, and multilingual support, with seamless switching between thinking and non-thinking modes for optimal performance in various scenarios, including mathematics, coding, and creative writing.

To run this model you first need to deploy it on a Dedicated Endpoint.

  • Model card

    Architecture Overview:
    • Mixture-of-Experts with 48 layers, 32 query heads, 4 key-value heads
    • 128 expert networks with 8 activated per token for efficient inference
    • 128K context window with sparse activation patterns
    • Expert routing system with learned gating functions

    Training Methodology:
    • Combined next-token prediction with expert specialization training
    • Different experts develop specialized capabilities in math, coding, science, and creative writing
    • Expert balancing techniques to prevent expert collapse
    • Reinforcement learning optimization for both expert utilization and response quality

    Performance Characteristics:
    • Superior parameter efficiency compared to dense alternatives
    • Achieves performance comparable to much larger models
    • Faster inference speeds with lower memory requirements
    • Dynamic computation allocation based on input complexity

  • Prompting

    Conversation Format:
    • Advanced system/user/assistant format with dynamic expert activation
    • Supports complex multi-turn dialogues with reasoning chains
    • Efficient inference through mixture-of-experts architecture
    • Strong performance on coding, mathematics, and creative tasks

    Expert Utilization:
    • Different experts activated based on input content and task requirements
    • Seamless switching between mathematical, coding, and linguistic experts
    • Contextual understanding with efficient resource allocation
    • Maintains conversation quality while optimizing computational efficiency

    Optimization Strategies:
    • Leverages specialized experts for domain-specific tasks
    • Benefits from explicit task specification in prompts
    • Responds well to structured reasoning requests
    • Optimized for both creative and analytical applications

  • Applications & use cases

    High-Performance Applications:
    • Enterprise conversational AI requiring efficient large-scale deployment
    • Advanced STEM education and tutoring with specialized knowledge domains
    • Sophisticated coding assistance and development tools
    • Creative writing and content generation for professional applications

    Technical Solutions:
    • Multilingual customer support with cultural context awareness
    • Research and analysis assistance across multiple disciplines
    • Complex reasoning tasks requiring expert-level knowledge
    • Applications demanding premium conversation quality with computational efficiency

    Specialized Domains:
    • Mathematical modeling and scientific computation
    • Code generation, review, and debugging assistance
    • Legal document analysis and regulatory compliance
    • Financial modeling and investment analysis tools

Related models
  • Model provider
    Qwen
  • Type
    Chat
  • Main use cases
    Chat
    Small & Fast
    Medium General Purpose
  • Fine tuning
    Supported
  • Deployment
    On-Demand Dedicated
    Monthly Reserved
  • Parameters
    30.5B
  • Activated parameters
    3.3B
  • Context length
    128K
  • Input modalities
    Text
  • Output modalities
    Text