gemma 4 26B A4B it qat q4 0 unquantized

Name: gemma 4 26B A4B it qat q4 0 unquantized
Author: Google

by GoogleGemma

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

Released April 2026context N/A26B params

A mid-sized multimodal model from Google's Gemma family that handles both text and image inputs. It operates at the 26B parameter scale in a quantized form (Q4_0 IQ4 NL), making it more accessible for local deployment while trading some precision for reduced memory footprint. Expect solid general-purpose reasoning and vision understanding within the constraints of aggressive quantization.

Capabilities

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

Instruction Following

Strong

Multimodal

gemma 4 26B A4B it qat q4 0 unquantized

by GoogleGemma

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

Released April 2026context N/A26B params

A mid-sized multimodal model from Google's Gemma family that handles both text and image inputs. It operates at the 26B parameter scale in a quantized form (Q4_0 IQ4 NL), making it more accessible for local deployment while trading some precision for reduced memory footprint. Expect solid general-purpose reasoning and vision understanding within the constraints of aggressive quantization.

Capabilities

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

Instruction Following

Strong

Multimodal

Glossary

General-PurposeDesigned to handle a wide variety of different tasks rather than being specialized for one specific domain.Local DeploymentRunning a model directly on your own computer or server instead of sending requests to a remote service.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.Parameter ScaleThe total number of trainable weights in a model, often expressed in billions (B); larger models generally have more capacity but require more computing power.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.Vision UnderstandingThe ability of an AI model to analyze and interpret visual information from images, identifying objects, scenes, and their relationships.

Capabilities

Capabilities

Use Case Fit

Glossary