Qwen3.6 35B A3B PRISM NVFP4

Qwen3.6

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

Released April 2026context N/A35B params

A quantized variant of Qwen's mixture-of-experts architecture, activating around 3 billion parameters per forward pass despite a 35 billion total parameter count. This makes it notably efficient at inference time — doing more with less compute. The NVFP4 precision format targets NVIDIA hardware specifically, trading some numerical fidelity for speed and memory savings.

Capabilities

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

Coding

Strong

Instruction Following

Strong

Factual Knowledge

Use Case Fit

Fit scores are AI-generated based on model capabilities, intended use, and technical specifications. Learn more

Qwen3.6 35B A3B PRISM NVFP4

Qwen3.6

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

Released April 2026context N/A35B params

A quantized variant of Qwen's mixture-of-experts architecture, activating around 3 billion parameters per forward pass despite a 35 billion total parameter count. This makes it notably efficient at inference time — doing more with less compute. The NVFP4 precision format targets NVIDIA hardware specifically, trading some numerical fidelity for speed and memory savings.

Capabilities

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

Coding

Strong

Instruction Following

Strong

Factual Knowledge

Use Case Fit

Fit scores are AI-generated based on model capabilities, intended use, and technical specifications. Learn more

Glossary

ArchitectureThe underlying structural design of a neural network that defines how data flows through layers and components.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.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.NVFP4 PrecisionA low-precision numerical format optimized by NVIDIA that uses fewer bits per number than standard formats, enabling efficient inference on NVIDIA GPUs while maintaining reasonable accuracy.NVFP4 PrecisionA low-precision numerical format that uses 4 bits per weight, developed by NVIDIA to compress models for efficient inference on consumer hardware.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.QuantizedA technique that reduces a model's size and memory usage by storing weights with lower precision (fewer bits), trading some accuracy for efficiency.