AI is happening. The future is updating.gemma-4-31B-it-NVFP4
- **Model Architecture:** Gemma4ForConditionalGeneration - **Input:** Text / Image - **Output:** Text - **Model Optimizations:** - **Weight quantization:** FP4 - **Activation quantization:** FP4 - **Release Date:** 2026-04-04 - **Version:** 1.0 - **Model Developers:** RedHatAI This model is a quantized version of google/gemma-4-31B-it. It was evaluated on several tasks to assess its quality in comparison to the unquantized model. This model was obtained by quantizing the weights and activations of google/gemma-4-31B-it to FP4 data type using the NVFP4 format, ready for inference with vLLM. This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 75%.
- **Model Architecture:** Gemma4ForConditionalGeneration - **Input:** Text / Image - **Output:** Text - **Model Optimizations:** - **Weight quantization:** FP4 - **Activation quantization:** FP4 - **Release Date:** 2026-04-04 - **Version:** 1.0 - **Model Developers:** RedHatAI This model is a quantized version of google/gemma-4-31B-it. It was evaluated on several tasks to assess its quality in comparison to the unquantized model. This model was obtained by quantizing the weights and activations of google/gemma-4-31B-it to FP4 data type using the NVFP4 format, ready for inference with vLLM. This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 75%.