Kubernetes received native support for AI inference through contributions from Google Cloud, ByteDance, and Red Hat. The platform now includes inference performance benchmarking, LLM-aware routing, inference gateway load balancing, and dynamic resource allocation to address LLMs, specialized hardware, and complex request/response patterns. Key projects include Inference Perf for accelerator benchmarking, the Gateway API Inference extension for LLM-aware routing, DRA for scheduling and fungibility across accelerators, and the vLLM library for LLM inference and serving. Inference servers are evolving from standalone deployments toward integrated and disaggregated serving models that better leverage Kubernetes capabilities.
Kubernetes has become the leading platform for deploying cloud-native applications and microservices, backed by an extensive community and comprehensive feature set for managing distributed systems. However, the rise of generative AI has introduced unique challenges for container orchestration. Large language models, specialized hardware, and demanding request/response patterns require a platform that is more than just a microservices manager. It needs to be "AI-aware."
This community-driven effort has equipped Kubernetes with a native understanding of AI inference, tackling critical areas like inference performance benchmarking, LLM-aware routing, inference gateway load balancing, and dynamic resource allocation. These foundational investments create a more robust and efficient platform for AI, allowing the entire ecosystem to benefit from the following: Benchmarking and qualification of accelerators with the Inference Perf project. Operationalizing scale-out architectures with LLM-aware routing via the Gateway API Inference extension.
Collection
[
|
...
]