Introducing Step 3.7 Flash: multimodal reasoning at scale

This post details the integration of Step 3.7 Flash, a 198B parameter sparse MoE vision-language model, into the Baseten Model Library. It highlights the hardware-efficient configuration achieved through FP8 quantization, enabling deployment on 4xH100 GPUs instead of the previously recommended 8xH200s. The post explains how MoE architecture, hybrid attention, and speculative decoding contribute to fitting the model and serving production traffic within this reduced hardware footprint, leading to lower cost per token and smoother autoscaling.