Qwen3 30B A3B Instruct 2507 FP4

Name: Qwen3 30B A3B Instruct 2507 FP4
Author: Qwen (Alibaba)

by Qwen (Alibaba)Qwen 3

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

Released May 2025262K context≈ 196,608 words30B params

A mixture-of-experts model that activates only 3 billion of its 30 billion parameters per forward pass, making it surprisingly efficient for its capability level. It handles long-context tasks well, supporting up to 262K tokens, and operates in a hybrid thinking/non-thinking mode that lets it toggle between quick responses and deeper reasoning. The FP4 quantization reduces memory footprint further, though with some precision trade-offs compared to full-precision variants.

Capabilities

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

Creative Writing

Moderate

Multilingual

Qwen3 30B A3B Instruct 2507 FP4

by Qwen (Alibaba)Qwen 3

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

Released May 2025262K context≈ 196,608 words30B params

A mixture-of-experts model that activates only 3 billion of its 30 billion parameters per forward pass, making it surprisingly efficient for its capability level. It handles long-context tasks well, supporting up to 262K tokens, and operates in a hybrid thinking/non-thinking mode that lets it toggle between quick responses and deeper reasoning. The FP4 quantization reduces memory footprint further, though with some precision trade-offs compared to full-precision variants.

Capabilities

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

Creative Writing

Moderate

Multilingual

Glossary

FP4 QuantizationA compression technique that represents model weights using only 4-bit floating-point numbers instead of larger formats, reducing memory usage and speeding up inference.Forward PassA single computation cycle where input data flows through the model's layers to produce an output prediction.Full-PrecisionA model using standard 32-bit floating-point numbers to represent weights, providing maximum accuracy but requiring more memory.Long-ContextThe ability of a model to process and understand very long sequences of text while maintaining coherence across distant parts of the input.Memory FootprintThe amount of RAM or storage space a model requires to run, which is critical for deployment on resource-constrained devices.Non-thinking ModeA model state that produces concise, direct answers without intermediate reasoning steps.ParametersThe learned numerical values in a model — more parameters generally means more capacity but higher compute cost.PrecisionThe level of numerical detail a model uses to represent its internal values; higher precision means more accurate calculations but requires more memory.QuantizationReducing a model's numerical precision (e.g., from 16-bit to 4-bit) to shrink memory usage and speed up inference.ReasoningThe model's ability to work through multi-step logical problems and provide justified answers rather than just pattern-matching.Thinking ModeA model operating mode where it explicitly works through problems step-by-step before generating a final answer, improving accuracy on complex tasks.TokensThe basic units of text that a language model processes, typically representing words or word fragments.

Capabilities

Capabilities

Use Case Fit

Glossary