Run Real-Time and Async Inference on the Same Infrastructure with GKE Inference Gateway

This article explores how Google Kubernetes Engine (GKE) Inference Gateway addresses the trade-off between cost and performance for AI serving workloads by treating accelerator capacity as a single, fluid resource pool that can handle both real-time and high-throughput async inference patterns.

Details

Traditional Kubernetes environments often handle real-time and async AI inference workloads using separate, siloed GPU and TPU accelerator clusters. This can lead to over-provisioning for real-time traffic and underutilization of resources for async tasks. GKE Inference Gateway addresses this challenge by treating accelerator capacity as a single, fluid resource pool that can serve both low-latency real-time and high-throughput async workloads. For real-time inference, Inference Gateway performs latency-aware scheduling based on real-time metrics like KV cache utilization to minimize time-to-first-token and ensure consistent performance. For async inference, the Async Processor Agent integrates with Inference Gateway to leverage idle accelerator capacity between real-time spikes, reducing resource fragmentation and hardware costs. This unified platform provides a cost-effective and efficient way to run the full spectrum of AI inference patterns on the same infrastructure.