With the brand new vLLM/TPU integration, you’ll be able to deploy your fashions on TPUs with out the necessity for intensive code modifications. A spotlight is the assist for the favored vLLM library on TPUs, permitting interoperability throughout GPUs and TPUs. By opening up the ability of TPUs for inference on GKE, Google Cloud is offering intensive decisions for patrons seeking to optimize their price-to-performance ratio for demanding AI workloads.
AI-aware load balancing with GKE Inference Gateway
In contrast to conventional load balancers that distribute site visitors in a round-robin style, GKE Inference Gateway is clever and AI-aware. It understands the distinctive traits of generative AI workloads, the place a easy request may end up in a prolonged, computationally intensive response.
The GKE Inference Gateway intelligently routes requests to essentially the most applicable mannequin duplicate, making an allowance for components like the present load and the anticipated processing time, which is proxied by the KV cache utilization. This prevents a single, long-running request from blocking different, shorter requests, a standard reason behind excessive latency in AI functions. The result’s a dramatic enchancment in efficiency and useful resource utilization.
