Qwen3 30B A3B

Name: Qwen3 30B A3B
Author: Qwen (Alibaba)

by Qwen (Alibaba)Qwen 3

Open WeightModel weights are publicly available — can be downloaded and self-hosted

Released April 202541K context≈ 30,720 words30B params

Qwen3 30B A3B is a mixture-of-experts model that activates only 3 billion parameters per forward pass despite having 30 billion total, making it punchy well above its computational weight. It supports a hybrid thinking mode, toggling between deliberate step-by-step reasoning and faster direct responses depending on the task. The trade-off is typical of sparse MoE architectures: memory footprint for loading all weights remains substantial even when inference is lean.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Instruction Following

Exceptional

Multilingual

Qwen3 30B A3B

by Qwen (Alibaba)Qwen 3

Open WeightModel weights are publicly available — can be downloaded and self-hosted

Released April 202541K context≈ 30,720 words30B params

Qwen3 30B A3B is a mixture-of-experts model that activates only 3 billion parameters per forward pass despite having 30 billion total, making it punchy well above its computational weight. It supports a hybrid thinking mode, toggling between deliberate step-by-step reasoning and faster direct responses depending on the task. The trade-off is typical of sparse MoE architectures: memory footprint for loading all weights remains substantial even when inference is lean.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Instruction Following

Exceptional

Multilingual

Glossary

Forward PassA single computation cycle where input data flows through the model's layers to produce an output prediction.Hybrid Thinking ModeA capability that allows a model to switch between fast, direct responses and slower, more deliberate reasoning depending on task complexity.InferenceThe process of running a trained model to generate predictions or outputs from new inputs.Memory FootprintThe amount of RAM or storage space a model requires to run, which is critical for deployment on resource-constrained devices.ParametersThe learned numerical values in a model — more parameters generally means more capacity but higher compute cost.ReasoningThe model's ability to work through multi-step logical problems and provide justified answers rather than just pattern-matching.Sparse MoEA mixture-of-experts design where only a small fraction of the model's parameters are used for each prediction, reducing computational cost while maintaining model capacity.Step-by-Step ReasoningAn approach where the model explicitly works through intermediate reasoning steps before arriving at a final answer, rather than jumping directly to conclusions.Thinking ModeA model operating mode where it explicitly works through problems step-by-step before generating a final answer, improving accuracy on complex tasks.WeightsThe numerical parameters inside a neural network that determine how it processes input and generates output.

Capabilities

Capabilities

Use Case Fit

Glossary