Triton Inference Server is a high-performance AI model inference server and multi-framework model runtime designed for deploying machine learning models across cloud, data center, and embedded edge infrastructure. It serves as an execution engine that allows for the concurrent running of models from various frameworks to optimize hardware utilization.
The project features a dynamic batching inference engine that groups individual requests into larger batches to increase total processing throughput. It also provides a model ensemble pipeline, which enables the chaining of multiple models together to create complex data processing and inference sequences.
The server covers broader capabilities including model lifecycle management through a central storage repository, performance monitoring for hardware utilization and latency, and the ability to integrate in-process via native APIs. It supports routing requests through standard web protocols and utilizes shared memory for efficient data exchange.