KServe Meets vLLM: Cloud-Native Scalable LLM Serving on K8s

KServe is a Kubernetes-native model serving platform offering enterprise-grade capabilities for predictive and generative AI—including autoscaling, ModelMesh routing, and standardized APIs across Hugging Face and vLLM models. Since the v0.13 release, KServe officially supports vLLM as a backend runtime, enabling high-performance LLM serving within the Kubernetes ecosystem. With the v0.15 release, KServe added advanced LLM-specific features including distributed KV caching via LMCache, LLM-aware autoscaling using KEDA, and integration with the Envoy AI Gateway for intelligent routing and traffic management.

KServe supports a plug-and-play architecture using custom ServingRuntimes and InferenceService CRDs, enabling optimized LLM serving via vLLM backends across GPUs (NVIDIA, AMD, Intel) and CPUs. Seamless integration matters — leverage vLLM's high-efficiency engine without rebuilding your stack while KServe handles orchestration, scaling, and routing. Features like LMCache maximize GPU throughput critical for LLMs. Offloading of KV-cache layers reduces inference costs and helps meet SLOs for latency and throughput. (in progress)