Qwen3.6 27B NVFP4

Qwen 3

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

Released April 2026context N/A27B params

A quantized variant of Qwen3's 32B model, trimmed to 27B parameters using NVFP4 precision by a community publisher. It trades a small amount of numerical precision for reduced memory footprint, making the weights more accessible on consumer hardware. Behavior and reasoning capabilities follow the base Qwen3 architecture, with the usual caveats around quantization potentially affecting edge-case outputs.

Capabilities

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

Multilingual

Exceptional

Tool Use

Strong

Coding

Use Case Fit

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

Qwen3.6 27B NVFP4

Qwen 3

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

Released April 2026context N/A27B params

A quantized variant of Qwen3's 32B model, trimmed to 27B parameters using NVFP4 precision by a community publisher. It trades a small amount of numerical precision for reduced memory footprint, making the weights more accessible on consumer hardware. Behavior and reasoning capabilities follow the base Qwen3 architecture, with the usual caveats around quantization potentially affecting edge-case outputs.

Capabilities

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

Multilingual

Exceptional

Tool Use

Strong

Coding

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.Memory FootprintThe amount of RAM or storage space a model requires to run, which is critical for deployment on resource-constrained devices.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.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.ReasoningThe model's ability to work through multi-step logical problems and provide justified answers rather than just pattern-matching.Reasoning CapabilitiesThe model's ability to work through multi-step problems methodically and show its thinking process rather than jumping to answers.WeightsThe numerical parameters inside a neural network that determine how it processes input and generates output.