gemma 4 31B it qat NVFP4 Blackwell

gemma

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

Released July 2026context N/A

A quantized variant of Google's Gemma 4 31B, optimized specifically for NVIDIA Blackwell hardware using INT4 quantization. It trades some precision for significantly reduced memory footprint and faster inference on compatible GPUs. Behavior should closely mirror the base Gemma 4 31B, though quantization artifacts may occasionally surface in nuanced or complex outputs.

Capabilities

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

Coding

Strong

Instruction Following

Strong

Reasoning & Logic

Use Case Fit

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

gemma 4 31B it qat NVFP4 Blackwell

gemma

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

Released July 2026context N/A

A quantized variant of Google's Gemma 4 31B, optimized specifically for NVIDIA Blackwell hardware using INT4 quantization. It trades some precision for significantly reduced memory footprint and faster inference on compatible GPUs. Behavior should closely mirror the base Gemma 4 31B, though quantization artifacts may occasionally surface in nuanced or complex outputs.

Capabilities

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

Coding

Strong

Instruction Following

Strong

Reasoning & Logic

Use Case Fit

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

Glossary

INT4 QuantizationA compression technique that reduces a model's size and memory usage by storing weights as 4-bit integers instead of higher-precision numbers, making it faster and cheaper to run with minimal accuracy loss.InferenceThe process of running a trained model to generate predictions or outputs from new inputs.Memory FootprintThe amount of RAM or storage space a model requires to run, which is critical for deployment on resource-constrained devices.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.Quantization ArtifactsErrors or degradation in model output that occur as a side effect of reducing precision through quantization.QuantizedA technique that reduces a model's size and memory usage by storing weights with lower precision (fewer bits), trading some accuracy for efficiency.