Qwen3 Next 80B A3B Instruct quantized.w4a16

Qwen 3

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

Released October 2022262K context≈ 196,608 words80B params

A large mixture-of-experts model that activates only 3 billion parameters per forward pass despite having 80 billion total, making it surprisingly efficient at inference time. It handles long contexts up to 262,144 tokens, so it can work through very lengthy documents without losing the thread. The w4a16 quantization reduces memory footprint further, though this comes with a small fidelity trade-off compared to full-precision weights.

Capabilities

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

Long Context

Moderate

Multimodal

Qwen3 Next 80B A3B Instruct quantized.w4a16

Qwen 3

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

Released October 2022262K context≈ 196,608 words80B params

A large mixture-of-experts model that activates only 3 billion parameters per forward pass despite having 80 billion total, making it surprisingly efficient at inference time. It handles long contexts up to 262,144 tokens, so it can work through very lengthy documents without losing the thread. The w4a16 quantization reduces memory footprint further, though this comes with a small fidelity trade-off compared to full-precision weights.

Capabilities

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

Long Context

Moderate

Multimodal

Glossary

FidelityThe degree to which a quantized or compressed model preserves the quality and accuracy of the original full-precision model.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.Full-Precision WeightsModel parameters stored at maximum numerical accuracy (typically 32-bit floating point), which provides the best quality but requires more memory and computation.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.TokensThe basic units of text that a language model processes, typically representing words or word fragments.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