gemma 4 31B it qat q4 0 unquantized assistant

Name: gemma 4 31B it qat q4 0 unquantized assistant
Author: Google

by GoogleGemma

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

Released May 2026context N/A31B params

A compact multimodal model that handles both text and image inputs, producing text output. It carries the efficiency-focused DNA of Google's Gemma family, with open weights under Apache 2.0 making it freely inspectable and deployable. The IQ4_0 quantization variant trades some precision for reduced memory footprint, which can affect output quality compared to the full-precision version.

Capabilities

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

Multimodal

Strong

Reasoning & Logic

gemma 4 31B it qat q4 0 unquantized assistant

by GoogleGemma

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

Released May 2026context N/A31B params

A compact multimodal model that handles both text and image inputs, producing text output. It carries the efficiency-focused DNA of Google's Gemma family, with open weights under Apache 2.0 making it freely inspectable and deployable. The IQ4_0 quantization variant trades some precision for reduced memory footprint, which can affect output quality compared to the full-precision version.

Capabilities

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

Multimodal

Strong

Reasoning & Logic

Glossary

Full-PrecisionA model using standard 32-bit floating-point numbers to represent weights, providing maximum accuracy but requiring more memory.Memory FootprintThe amount of RAM or storage space a model requires to run, which is critical for deployment on resource-constrained devices.MultimodalA model that can process and understand multiple types of input, such as both text and images.Multimodal ModelAn AI model that can process and understand multiple types of input data, such as video, images, and text together.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.WeightsThe numerical parameters inside a neural network that determine how it processes input and generates output.

Capabilities

Capabilities

Use Case Fit

Glossary