Qwen3 30B A3B Instruct 2507 quantized.w4a16

Qwen 3

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

Released December 2025262K context≈ 196,608 words

A mixture-of-experts model that activates only 3B parameters per forward pass despite having 30B total, making it surprisingly efficient at inference time. It handles a massive 262K token context window, so it can work through very long documents without losing the thread. The W4A16 quantization means weights are stored at 4-bit precision, trading a small amount of accuracy for significantly reduced memory footprint.

Capabilities

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

Long Context

Exceptional

Factual Knowledge

Qwen3 30B A3B Instruct 2507 quantized.w4a16

Qwen 3

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

Released December 2025262K context≈ 196,608 words

A mixture-of-experts model that activates only 3B parameters per forward pass despite having 30B total, making it surprisingly efficient at inference time. It handles a massive 262K token context window, so it can work through very long documents without losing the thread. The W4A16 quantization means weights are stored at 4-bit precision, trading a small amount of accuracy for significantly reduced memory footprint.

Capabilities

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

Long Context

Exceptional

Factual Knowledge

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.Context WindowThe maximum number of tokens a model can process in a single conversation or prompt.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.TokenA small unit of text (a word, subword, or punctuation mark) that a language model breaks input into for processing.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