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Scaling Recommendation System Inference with Merlin Hierarchical Parameter Server

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By Shashank Verma, Wenwen Gao, Matthias Langer, Yingcan Wei
| NVIDIA
Recommendation systems are widely used today to personalize user experiences and improve customer engagement in various settings like e-commerce, social media, and news feeds. Serving user requests with low latency and high accuracy is hence critical to sustain user engagement. This includes performing high-speed lookups and computations while seamlessly refreshing models with the newest updates, which is especially challenging for large-scale recommenders where the model size exceeds GPU memory.
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