Evolving Kubernetes for generative AI inference

With the new vLLM/TPU integrationyou can deploy your models on TPUs without the need for extensive code modifications. A highlight is the support for the popular vLLM library on TPUs, allowing interoperability across GPUs and TPUs. By opening up the power of TPUs for inference on GKE, Google Cloud is providing extensive choices for customers looking to optimize their price-to-performance ratio for demanding AI workloads.

AI-aware load balancing with GKE Inference Gateway

Unlike traditional load balancers that distribute traffic in a round-robin fashion, GKE Inference Gateway is intelligent and AI-aware. It understands the unique characteristics of generative AI workloads, where a simple request can result in a lengthy, computationally intensive response.

The GKE Inference Gateway intelligently routes requests to the most appropriate model replica, taking into account factors like the current load and the expected processing time, which is proxied by the KV cache utilization. This prevents a single, long-running request from blocking other, shorter requests, a common cause of high latency in AI applications. The result is a dramatic improvement in performance and resource utilization.