Cloud Blog: Implementing High-Performance LLM Serving on GKE: An Inference Gateway Walkthrough

Source URL: https://cloud.google.com/blog/topics/developers-practitioners/implementing-high-performance-llm-serving-on-gke-an-inference-gateway-walkthrough/
Source: Cloud Blog
Title: Implementing High-Performance LLM Serving on GKE: An Inference Gateway Walkthrough

Feedly Summary: The excitement around open Large Language Models like Gemma, Llama, Mistral, and Qwen is evident, but developers quickly hit a wall. How do you deploy them effectively at scale?
Traditional load balancing algorithms fall short, as they fail to account for GPU/TPU load status, leading to inefficient routing for computationally intensive AI inference with its highly variable processing times. This directly impacts serving performance and the user experience.
This guide demonstrates how Google Kubernetes Engine and the new GKE Inference Gateway together offer a robust and optimized solution for high-performance LLM serving, specifically by overcoming the limitations of traditional load balancing with smart routing aware of AI-specific metrics like pending prompt requests and critical KV Cache utilization.
We’ll walk through deploying an LLM using the popular vLLM framework as the inference backend. We’ll use Google’s gemma-3-1b-it model and NVIDIA L4 GPUs as a concrete, easy-to-start example (avoiding the need for special GPU quota requests initially). The principles and configurations shown here apply directly to larger, more powerful models and diverse hardware setups.
Why Use GKE Inference Gateway for LLM Serving?
GKE Inference Gateway isn’t just another ingress controller; it’s purpose-built for the unique demands of generative AI workloads on GKE. It extends the standard Kubernetes Gateway API with critical features:

Intelligent load balancing: Goes beyond simple round-robin. Inference Gateway understands backend capacity, including GPU-specific metrics like KV-Cache utilization, to route requests optimally. For LLMs, the KV-Cache stores the intermediate attention calculations (keys and values) for previously processed tokens. This cache is the primary consumer of GPU memory during generation and is the most common bottleneck. By routing requests based on real-time cache availability, the gateway avoids sending new work to a replica that is near its memory capacity, thus preventing performance degradation and maximizing GPU usage, increasing throughput, and reducing latency.
AI-aware resource management: Inference Gateway recognizes AI model serving patterns. This enables advanced use cases like serving multiple different models or fine-tuned variants behind a single endpoint. It is particularly effective at managing and multiplexing numerous LoRA adapters on a shared pool of base models. This architecture dramatically increases model density on shared accelerators, reducing costs and operational complexity when serving many customized models. It also enables sophisticated, model-aware autoscaling strategies (beyond basic CPU/memory).
Simplified operations: Provides a dedicated control plane optimized for inference. It seamlessly integrates with GKE, offers specific inference dashboards in Cloud Monitoring, and supports optional security layers like Google Cloud Armor and Model Armor, reducing operational overhead.
Broad model compatibility: The techniques shown work with a wide array of Hugging Face compatible models.
Flexible hardware choices: GKE offers access to various NVIDIA GPU types (L4, A100, H100, etc.), allowing you to match hardware resources to your specific model size and performance needs. (See GPU platforms documentation).

The Walkthrough: Setting Up Your Inference Pipeline
Let’s get started building out our inference pipeline. By following these steps, you will deploy and configure the essential infrastructure to serve your LLMs with the high performance and scalability demanded by real-world applications, built on GKE and optimized by the Inference Gateway.
Environment Setup
Ensure your Google Cloud environment is ready. All steps in this walkthrough are tested in Google Cloud Shell. Cloud Shell has the Google Cloud CLI, kubectl, and Helm pre-installed.
1. Google Cloud project: Have a project with billing enabled.

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