NVIDIA Triton Inference Server — what makes it fast?

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Answer

$(1) Multi-framework: TensorRT, ONNX, PyTorch, TensorFlow, custom Python backend.$
$(2) Dynamic batching: request coalescing without user awareness.$
$(3) Concurrent model execution on same GPU (model parallelism).$
$(4) Model ensembling: pipeline models server-side.$
$(5) HTTP + gRPC.$
$(6) Metrics for Prometheus.$
$(7) Model repository: hot-reload versions.$
$Backed by NVIDIA + open-source.$
$Standard for GPU serving at scale.$

Check yourself — multiple choice

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Multi-framework (TRT/ONNX/PyTorch/TF/Python) + dynamic batching + concurrent model on GPU + ensembles + HTTP/gRPC + Prometheus + hot-reload versions; NVIDIA + OSS; standard GPU serving
Just PyTorch
Not real

Triton: multi-framework + dynamic batch + concurrent + ensembles + Prometheus.

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