PyTorch(@PyTorch):Generative recommender systems reformulate personalization as sequence modeling over user behavior, and NVIDIA recsys-examples now provides an end-to-end HSTU inference workflow with Dynamo-Triton. PyTorch AOTI compiles HSTU ranking models into native C++ artifacts, while FlexKV-backed KV caching stores reusable attention state to avoid recomputing long user histories. On an @nvidia RTX PRO 6000 Blackwell Workstation Edition GPU, the eight-layer HSTU model achieved up to 5.93x lower latency at batch size 8 with a 100% GPU KV-cache hit rate compared to the same AOTI configuration without caching. Read the full blog post:

2026.10.08 21:00

Generative recommender systems reformulate personalization as sequence modeling over user behavior, and NVIDIA recsys-examples now provides an end-to-end HSTU inference workflow with Dynamo-Triton. PyTorch AOTI compiles HSTU ranking models into native C++ artifacts, while FlexKV-backed KV caching stores reusable attention state to avoid recomputing long user histories. On an @nvidia RTX PRO 6000 Blackwell Workstation Edition GPU, the eight-layer HSTU model achieved up to 5.93x lower latency at batch size 8 with a 100% GPU KV-cache hit rate compared to the same AOTI configuration without caching. Read the full blog post: