gemma 4 26B A4B it FP8 dynamic Model Overview Model Architecture: Gemma4ForConditionalGeneration Input: Text / Image Output: Text Model Optimizations: Weight quantization: FP8 Activation quantization: FP8 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 FP8 data type using dynamic per token quantization, ready for inference with vLLM. This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%. Weights are quantized statically using per channel FP8 scaling, and activations are quantized dynamically at inference time using per token 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…
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