Qwen3 Coder 30B A3B Instruct FP4

Name: Qwen3 Coder 30B A3B Instruct 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 coding-focused model that punches above its weight through sparse activation — only 3 billion parameters fire per token despite a 30 billion parameter pool, keeping inference lean. It handles long codebases comfortably with a 262k token context window, making it practical for repository-scale tasks. The FP4 quantization trades a small amount of precision for significantly reduced memory footprint.

Capabilities

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

Creative Writing

Moderate

Tool Use

Qwen3 Coder 30B A3B Instruct 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 coding-focused model that punches above its weight through sparse activation — only 3 billion parameters fire per token despite a 30 billion parameter pool, keeping inference lean. It handles long codebases comfortably with a 262k token context window, making it practical for repository-scale tasks. The FP4 quantization trades a small amount of precision for significantly reduced memory footprint.

Capabilities

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

Creative Writing

Moderate

Tool Use

Glossary

Context WindowThe maximum number of tokens a model can process in a single conversation or prompt.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.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.Parameter PoolThe total set of learnable weights in a model; in sparse models, only a subset of this pool is activated for any given input.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.Sparse ActivationA technique where only a subset of a model's parameters are used for each input, reducing computational cost while maintaining performance.TokenA small unit of text (a word, subword, or punctuation mark) that a language model breaks input into for processing.

Capabilities

Capabilities

Use Case Fit

Glossary