Detailed Introduction
vLLM Production Stack is a production-oriented reference system designed to provide Kubernetes-native cluster deployment patterns and community-driven performance optimizations for vLLM. It combines container orchestration, scheduling strategies, GPU resource management, inference service composition, and monitoring to help teams reliably run vLLM-based models in production.
Main Features
- Kubernetes-native deployment with Helm/Operator integration.
- Performance tuning and scheduling recommendations for inference workloads to optimize GPU utilization and I/O.
- Monitoring, logging, and metrics collection for capacity planning and troubleshooting.
- Community-driven best practices to enable reuse and scaling across different cluster sizes.
Use Cases
Suitable for running large-model inference on Kubernetes clusters, including online low-latency inference, batch processing, and concurrent model serving. It is especially useful for teams that want to operate vLLM as a cluster service and require fine-grained control over GPU resources and performance.
Technical Features
- Built on containerization and Kubernetes primitives (scheduling, CSI, Operator) for extensibility.
- System-level optimizations focused on inference latency and throughput, including multi-instance GPU sharing and memory/I/O strategies.
- Integrates with existing monitoring and logging systems to support metrics-driven autoscaling and performance forensics.