Boosting AI Performance: Networking for AI Inference
𝗦𝘂𝗺𝗺𝗮𝗿𝘆: Victor Moreno, Product Manager for Cloud Networking at Google, discusses the critical role of networking in supporting AI inference. Learn how Google Cloud is implementing AI-aware traffic routing, specialized load balancing, and service extensions to optimize GPU utilization, minimize latency, and streamline governance for modern AI workloads.
𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲: Traditional networking approaches are ill-equipped for AI inference. Unlike standard web traffic, AI workloads are highly variable in size, and typical metrics like CPU usage fail to reflect actual GPU saturation. Relying on standard round-robin load balancing often leads to sending traffic to congested replicas, causing latency and inefficiency. Furthermore, developers face friction when managing multiple models with different APIs, and organizations struggle to enforce security guardrails without creating complex, disjointed network topologies.
𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: To solve these scaling issues, Google Cloud utilizes the GKE Inference Gateway and AI-aware load balancing. This architecture moves beyond simple request distribution by utilizing inference-specific metrics like KV cache utilization and queue depth. It introduces advanced capabilities such as prefix caching (routing prompts to replicas with pre-existing context), body-based routing for model identification, and LoRA adapter awareness. Additionally, the network layer now supports "Service Extensions," allowing the seamless insertion of API management and AI guardrails directly into the traffic flow.
𝗥𝗲𝘀𝘂𝗹𝘁𝘀: By adopting an AI-optimized networking strategy, organizations can achieve a dramatic improvement in resource efficiency and user experience. The approach minimizes "cold starts" by intelligently routing traffic, reduces total cost of ownership by maximizing GPU saturation, and accelerates developer velocity through unified APIs. Security is also strengthened, as guardrails can sanitize prompts and responses at the network edge before they ever reach the model or the end-user, saving compute costs on invalid requests.
𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗵𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀 𝗮𝗻𝗱 𝗸𝗲𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀 𝗳𝗿𝗼𝗺 𝗼𝘂𝗿 𝗳𝘂𝗹𝗹 𝗽𝗿𝗲𝘀𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝗩𝗶𝗰𝘁𝗼𝗿 𝗠𝗼𝗿𝗲𝗻𝗼, 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗿 𝗮𝘁 𝗚𝗼𝗼𝗴𝗹𝗲:
→ “A GPU or TPU could be fully utilized and that would not be visible with traditional metrics. So without the right metrics, a load balancer could blindly send traffic to replicas that are effectively congested. The inference gateway uses metrics like KV cache utilization … utilizing these specialized metrics, the least loaded replicas are identified and stack ranked.”
→ “The load balancer also keeps a shadow copy of the prefix caches that are in every replica… The inference gateway can reuse prefill computations that have been done before and rely on the commonality of different prompt requests to reduce GPU utilization.”
→ “One very important function to insert are AI guardrails to sanitize prompts and responses. When the prompt arrives, it sends it to a guardrail service… to check if the prompt is within policy. If so, the request is dropped and an error is returned. You don't even send the prompt to the model and spend the money on the GPU usage.”
𝗚𝗼𝗼𝗴𝗹𝗲 𝗖𝗹𝗼𝘂𝗱 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝘀 𝘂𝘀𝗲𝗱: GKE Inference Gateway, Cloud Load Balancing, Google Kubernetes Engine (GKE)
𝗟𝗲𝗮𝗿𝗻 𝗺𝗼𝗿𝗲:
→ Learn more about AI Inference on Google Cloud: https://cloud.google.com/discover/what-is-ai-inference
→ Explore Cloud Load Balancing: https://cloud.google.com/load-balancing
→ Read about GKE Enterprise: https://cloud.google.com/kubernetes-engine
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