Gemma 4 26B A4B NVFP4

Name: Gemma 4 26B A4B NVFP4
Author: NVIDIA

by NVIDIAGemma

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

Released May 2026context N/A

A compact, open-weight model from NVIDIA's quantized model lineup, optimized with NV-FP4 precision to run efficiently on NVIDIA hardware. The FP4 quantization means it trades some numerical precision for significantly reduced memory footprint and faster inference, making it practical for deployment on consumer or workstation GPUs. It handles text-in, text-out tasks and ships in the safetensors format, ready for straightforward integration.

Capabilities

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

Tool Use

Moderate

Factual Knowledge

Use Case Fit

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

Gemma 4 26B A4B NVFP4

by NVIDIAGemma

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

Released May 2026context N/A

A compact, open-weight model from NVIDIA's quantized model lineup, optimized with NV-FP4 precision to run efficiently on NVIDIA hardware. The FP4 quantization means it trades some numerical precision for significantly reduced memory footprint and faster inference, making it practical for deployment on consumer or workstation GPUs. It handles text-in, text-out tasks and ships in the safetensors format, ready for straightforward integration.

Capabilities

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

Tool Use

Moderate

Factual Knowledge

Use Case Fit

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

Glossary

FP4 PrecisionA ultra-low precision format using 4-bit floating-point numbers to represent model weights, enabling extreme compression.FP4 QuantizationA compression technique that represents model weights using only 4-bit floating-point numbers instead of larger formats, reducing memory usage and speeding up inference.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.Open-Weight ModelA model whose trained weights are publicly released, allowing anyone to download and run it locally.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.SafetensorsA safe, fast file format for storing model weights, designed to prevent code execution vulnerabilities.Safetensors FormatA secure and efficient file format for storing model weights that prioritizes safety and speed when loading models.Text-In, Text-OutA model that accepts text as input and produces text as output, without support for images, audio, or other data types.