Improving Ray Serve LLM on GKE throughput, latency
Developers looking for LLM inference and model serving often turn to Ray Serve, a scalable model serving library with developer-friendly, Python-native APIs built by Anyscale. Combined with Google Kubernetes Engine (GKE), developers have a powerful, unified platform optimized for demanding LLM serving use cases, spanning from initial model development to online production serving.
However, that flexibility and feature set used to come at a cost to performance. But today, in partnership with Anyscale, we are delivering up to 5x higher throughput and 8x lower latency in Ray Serve, meeting the growing demands and rigorous performance requirements of state-of-the-art distributed inference, without having to sacrifice ease of use.
Scaling inference without the bottlenecks
Through our joint engineering partnership, we are introducing three major architectural optimizations that dramatically improve Ray Serve LLM's performance characteristics:
- Ray Serve HAProxy integration: Ray Serve now builds in HAProxy to manage internal request routing...
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