Navigating the Complexity of AI Inference on Kubernetes with NVIDIA Grove
Understanding the Evolution of AI Inference Systems Artificial intelligence (AI) inference has shifted significantly from simple, single-model deployments to intricate systems involving multiple components. These components include prefill stages, decoding mechanisms, vision encoders, and key-value routers, among others. This transformation reflects the growing demands placed on AI to handle diverse tasks and complex data inputs. The Challenge of Managing Complex AI Pipelines With AI inference systems becoming multi-component and sometimes agentic, managing these pipelines presents a challenge. Each component may require specific resources and configurations. Coordinating them to work seamlessly demands careful orchestration, especially when deployed at scale. Without effective management, performance bottlenecks and inefficiencies can arise. Kubernetes as a Platform for AI Inference Deployment Kubernetes offers a container orchestration platform that can manage distributed ...