• How we cut Vertex AI latency by 35% with GKE Inference Gateway Product Manager Software Engineer Our most intelligent model is now available on Vertex AI and Gemini Enterprise As generative AI moves from experimentation to production, platform engineers face a universal challenge for inference serving: you need low latency, high throughput, and manageable costs. • It is a difficult balance. • Traffic patterns vary wildly, from complex coding tasks that require processing huge amounts of data, to quick, chatty conversations that demand instant replies. • Standard infrastructure often struggles to handle both efficiently. • Our solution:To solve this, the Vertex AI engineering team adopted theGKE Inference Gateway. • Built on the standard Kubernetes Gateway API, Inference Gateway solves the scale problem by adding two critical layers of intelligence: Load-aware routing:It scrapes real-time metrics (like KV Cache utilization) directly from the model server’s Prometheus endpoints to route requests to the pod that can serve them fastest.
Article Summaries:
- How we cut Vertex AI latency by 35% with GKE Inference Gateway Fisayo Feyisetan Product Manager Yao Yuan Software Engineer As generative AI moves from experimentation to production, platform engineers face a universal challenge for inference serving: you need low latency, high throughput, and manageable costs. It is a difficult balance. Traffic patterns vary wildly, from complex coding tasks that require processing huge amounts of data, to quick, chatty conversations that demand instant replies. Standard infrastructure often struggles to handle both efficiently. Our solution: To solve this, th
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