gemma 4 26B A4B it NVFP4 Model Overview 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 26B A4B it. It was evaluated on several tasks to assess its quality in comparison to the unquantized model. Model Optimizations This model was obtained by quantizing the weights and activations of google/gemma 4 26B A4B it to NVFP4 data type, 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%. Weights are quantized to FP4 with a group size of 16, and activations are quantized to FP4 with local per group scaling. Only the weights and activations of the linear operators within transformer blocks are quantized using LLM Compressor. Vision tower, embedding, output head, and MoE router layers are kept in their original precision. Deployment Use with vLLM This model can be deployed using vLLM. For detailed instructions including multimodal inference, thin…
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