Part 8/13:
Efficient Batching & Dynamic Models: Supports dynamic batching of requests, enabling better GPU utilization. Models are stored in a simple repository structure, where multiple versions and configurations can be managed easily.
Metrics & Monitoring: Provides detailed metrics like GPU utilization, request latency, request counts, enabling auto-scaling and load balancing through integrations with Prometheus and Kubernetes.
Mig illustrated Triton’s architecture with a diagram showing how clients send queries via HTTP/GRPC, the server batches requests, forwards them to model backends, and returns results—all while collecting metrics for performance tuning.