Deploying LLMs on Kubernetes: vLLM, Ray Serve & GPU Scheduling Guide (2026)

The comprehensive guide delves into deploying large language models (LLMs) on Kubernetes, addressing common pitfalls and advanced strategies for optimized performance and resource management. It explains the advantage of using Kubernetes for LLM inference, particularly for scaling and efficient GPU workload management, by leveraging tools like the NVIDIA GPU Operator for automatic GPU discovery and scheduling, and Ray Serve for multi-node and multi-model serving. The guide emphasizes the importance of metrics beyond CPU usage, advocating for scaling based on queue depth and GPU cache utilization to prevent inference bottlenecks. It provides detailed instructions for setting up GPU scheduling with Multi-Instance GPU (MIG) and time-slicing, alongside topology-aware scheduling for latency-sensitive tasks. Additionally, it covers production patterns such as canary rollouts, graceful shutdowns, and security hardening, while also discussing the use of Prometheus and Grafana for monitoring. For simpler scenarios or single-node deployments, standalone vLLM may suffice, but for complex, large-scale operations, it suggests using Ray Serve or even llm-d for disaggregated tasks. The guide highlights the role of Kubernetes operators like KubeRay for autoscaling and efficient resource management, noting that advanced setups can greatly benefit from Prometheus custom metrics and KEDA for effective autoscaling, including scaling to zero to manage costs.

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