Qwen3 30B A3B.w8a8

Qwen3

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

Released April 202541K context≈ 30,720 words30B params

A quantized variant of Alibaba's Qwen3 30B mixture-of-experts model, activating roughly 3 billion parameters per forward pass despite its larger total parameter count. This w8a8 quantization (8-bit weights and activations) trades a small amount of precision for significantly reduced memory footprint, making the model more accessible on consumer hardware.

Capabilities

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

Multilingual

Moderate

Tool Use

Qwen3 30B A3B.w8a8

Qwen3

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

Released April 202541K context≈ 30,720 words30B params

A quantized variant of Alibaba's Qwen3 30B mixture-of-experts model, activating roughly 3 billion parameters per forward pass despite its larger total parameter count. This w8a8 quantization (8-bit weights and activations) trades a small amount of precision for significantly reduced memory footprint, making the model more accessible on consumer hardware.

Capabilities

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

Multilingual

Moderate

Tool Use

Glossary

Forward PassA single computation cycle where input data flows through the model's layers to produce an output prediction.Memory FootprintThe amount of RAM or storage space a model requires to run, which is critical for deployment on resource-constrained devices.Parameter CountThe total number of adjustable weights in a model; more parameters generally mean more capacity to learn, but also require more computing power.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.QuantizedA technique that reduces a model's size and memory usage by storing weights with lower precision (fewer bits), trading some accuracy for efficiency.W8A8 QuantizationA specific quantization method where both weights (w) and activations (a) are stored as 8-bit integers, providing a good balance between memory savings and model quality.W8A8 QuantizationA specific quantization method that reduces both weights and activations to 8-bit integers, enabling faster computation on specialized hardware while maintaining reasonable accuracy.WeightsThe numerical parameters inside a neural network that determine how it processes input and generates output.

Capabilities

Capabilities

Use Case Fit

Glossary