triton inference

Triton Inference Server is NVIDIA's production inference platform supporting multiple model formats, dynamic batching, model ensembles, and GPU scheduling for high-throughput, low-latency model serving at scale. Multi-framework support: serves TensorFlow, PyTorch, TensorRT, ONNX, and custom backends from single server; standardized inference API regardless of framework. Dynamic batching: automatically batches concurrent requests to maximize GPU utilization; configurable maximum batch size and delay. Model repository: organizes models with versioning; supports hot reload of new model versions without downtime. Ensemble models: chain multiple models where output of one feeds into another; complex pipelines as single endpoint. GPU scheduling: intelligent placement of models across GPUs; instance groups control model replicas and GPU assignment. Backend flexibility: built-in backends for common frameworks plus Python backend for custom logic; extensible architecture. Metrics: Prometheus metrics for latency, throughput, queue depth, and GPU utilization; essential for production monitoring. Client libraries: C++, Python, and Java clients for easy integration. HTTP/gRPC: supports both protocols for different integration needs. Concurrent model execution: multiple models on same GPU with memory management. Triton is the standard for NVIDIA GPU inference serving in production environments.