Qwen3 30B A3B quantized.w4a16

Qwen 3

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

Released October 202241K context≈ 30,720 words30B params

A mixture-of-experts model that activates only 3 billion of its 30 billion parameters per forward pass, making it notably efficient at inference time without sacrificing the breadth of a larger model. The w4a16 quantization compresses weights to 4-bit precision while keeping activations at 16-bit, reducing memory footprint considerably.

Capabilities

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

Creative Writing

Moderate

Multimodal

Qwen3 30B A3B quantized.w4a16

Qwen 3

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

Released October 202241K context≈ 30,720 words30B params

A mixture-of-experts model that activates only 3 billion of its 30 billion parameters per forward pass, making it notably efficient at inference time without sacrificing the breadth of a larger model. The w4a16 quantization compresses weights to 4-bit precision while keeping activations at 16-bit, reducing memory footprint considerably.

Capabilities

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

Creative Writing

Moderate

Multimodal

Glossary

4-bit PrecisionA quantization level where model weights are stored using only 4 bits per value, significantly reducing model size at the cost of some accuracy.Bit PrecisionThe number of bits used to represent each number in a model; lower bit precision (like 3-bit) means smaller file size but potentially less accurate calculations.Forward PassA single computation cycle where input data flows through the model's layers to produce an output prediction.InferenceThe process of running a trained model to generate predictions or outputs from new inputs.Inference TimeThe amount of time it takes for a model to process input and generate output after it has been trained.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.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.W4A16A quantization format where model weights are stored in 4-bit precision while calculations use 16-bit precision, balancing efficiency with accuracy.W4A16 QuantizationA specific quantization scheme where weights are stored in 4-bit precision while activations remain in 16-bit precision, balancing memory savings with accuracy.WeightsThe numerical parameters inside a neural network that determine how it processes input and generates output.

Capabilities

Capabilities

Use Case Fit

Glossary