A developer needs to run a PyTorch model and an ONNX model side by side on the same Triton Inference Server instance, serving both through a single HTTP endpoint prefix. What aspect of Triton makes this directly possible?
- ATriton's concurrent model execution, which lets multiple model instances share GPU memory and run simultaneously.
- BTriton's model ensemble feature, which links the PyTorch and ONNX models into a single composite pipeline.
- CTriton's model repository layout, which stores each model in its own versioned subdirectory and serves all of them under one server.
- DTriton's multiple framework backend support, which allows models from different frameworks to be loaded and served concurrently within one server process. Correct
Why A is wrong: Concurrent model execution relates to running multiple instances of the same or different models at the same time for throughput, which is tempting here, but it is not the feature that enables different framework backends to coexist. Concurrent execution relies on the multi-backend architecture but is a distinct capability.
Why B is wrong: Ensembles are plausible because they connect multiple models, but an ensemble requires explicitly defining the data flow between models. Simply running two unrelated models side by side does not require an ensemble, and an ensemble would not be needed just to serve them independently.
Why C is wrong: The model repository structure is a prerequisite for loading models, and it is tempting because it is what you configure to add models. However, the repository layout alone does not explain why different framework types can coexist; that is determined by the backend architecture.
Why D is correct: Triton ships with separate backends for PyTorch (libtorch), ONNX Runtime, TensorFlow, TensorRT, and others. Each backend is a shared library loaded on demand, so models from different frameworks coexist in the same server process and are accessible through the same API endpoint without any conversion.