Gemma 4 on Cerebras: Fast Multimodal AI

This post demonstrates the practical application of Gemma 4 31B on Cerebras hardware, achieving ~2,300 tokens/sec for multimodal inference. It details three use cases: document analysis (achieving a 17x speedup over GPU providers), image search, and a rental car damage scout application that processes video frames for damage detection within 5 seconds. The post highlights the importance of designing the entire application for speed, not just the model endpoint, and provides seven tips for building fast multimodal applications, including prompt templating, generation defaults, intentional thinking mode, clean history management, image-before-text ordering, structured long context input, and simple tool loops. The core contribution is showcasing how low-latency multimodal inference enables interactive product experiences and changes the design possibilities for developers.