gemma 4 31B it FP8

Name: gemma 4 31B it FP8
Author: Google

by Googlegemma

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

Released April 2026context N/A31B params

A mid-sized open-weight model from Google's Gemma 4 family, quantized to FP8 precision by protoLabsAI for more efficient deployment. The FP8 quantization means it runs faster and uses less memory than the full-precision version, with a modest trade-off in output fidelity. It handles text tasks competently and suits environments where hardware constraints matter.

Capabilities

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

Factual Knowledge

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 FP8

by Googlegemma

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

Released April 2026context N/A31B params

A mid-sized open-weight model from Google's Gemma 4 family, quantized to FP8 precision by protoLabsAI for more efficient deployment. The FP8 quantization means it runs faster and uses less memory than the full-precision version, with a modest trade-off in output fidelity. It handles text tasks competently and suits environments where hardware constraints matter.

Capabilities

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

Factual Knowledge

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

FP8 PrecisionA data format that stores numbers using 8 bits instead of the standard 32 bits, significantly reducing memory requirements with minimal quality loss.FP8 QuantizationA compression technique that reduces model size by representing weights using 8-bit floating-point numbers instead of higher precision, making it faster and more memory-efficient.FidelityThe degree to which a quantized or compressed model preserves the quality and accuracy of the original full-precision model.Full-PrecisionA model using standard 32-bit floating-point numbers to represent weights, providing maximum accuracy but requiring more memory.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.