New GKE inference capabilities reduce costs, tail latency and increase throughput

When it comes to AI, inference is where today’s generative AI models can solve real-world business problems. Google Kubernetes Engine (GKE) is seeing increasing adoption of gen AI inference. For example, customers like HubX run inference of image-based models to serve over 250k images/day to power gen AI experiences, and Snap runs AI inference on GKE for its ad ranking system. 

However, there are challenges when deploying gen AI inference. First, during the evaluation phase of this journey, you have to evaluate all your accelerator options. You need to choose the right one for your use case. While many customers are interested in using Tensor Processing Units (TPU), they are looking for compatibility with popular model servers. Then, once you’re in production, you need to load-balance traffic, manage price-performance with real traffic at scale, monitor performance, and debug any issues that arise...

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