HSTU Generative Recommenders on Triton

Hierarchical Sequential Transduction Units (HSTU) power Generative Recommenders (GRs): recommendation workloads reformulated as generative modeling over high-cardinality, non-stationary event streams. HSTU supports both retrieval and ranking style tasks.

Triton Inference Server can serve HSTU models through the PyTorch backend using ahead-of-time (AOT) Inductor packages (platform: "torch_aoti"). Training, export, KV-cache runtime, and end-to-end examples live in NVIDIA's recsys-examples repository rather than in this tutorials tree.

Where to go next

Resource	Description
HSTU overview	Architecture, training, and inference entry points
HSTU inference	Inference features, KV-cache, AOTInductor export, and KuaiRand examples
PyTorch AOTI on Triton	torch_aoti model repository layout and configuration