Criteo x NVIDIA AI Meetup | LLM-Powered Recommendations at Scale

​Overview

​Join NVIDIA and Criteo to hear how teams take LLM-powered recommendations from prototype to production. Learn what it really takes to deliver relevance at scale, from inference stack design to optimizing open-weight models on-premises, with candid lessons from the builders.

​Agenda

• ​6:00 PM - Door openings

• ​6:30 PM - Welcoming Remarks by Criteo and Nvidia

• ​6:45 PM - How to Build a High-Throughput, Low-Latency Product Relevance Classifier On-Premises w/ Criteo

• ​7:15 PM - Advancing Generative Recommendation Inference w/ NVIDIA

• ​7:45 PM - Panel Discussion

• ​8:15 PM - Networking, food and drinks

​Sessions

​Advancing Generative Recommendation Inference
Marek Pikulski, Senior AI Developer Technology Engineer, NVIDIALearn how NVIDIA and Spotify engineered a production-grade inference stack for LLM-powered personalized recommendations. This talk covers the specific challenges of running beam search, multi-item candidate scoring, and user soft token injection at scale, and how native support for these patterns was built into TensorRT LLM. We’ll walk through the architecture decisions, open-source contributions, and the benchmarks that made the case for the switch.

How to Build a High-Throughput, Low-Latency Product Relevance Classifier On-Premises
Zakaria Ouzarf, Software Development Engineer, Criteo
Marius Alonso, Machine Learning Engineer, Criteo
How can you build an open-weight LLM-based product relevance classifier that matches the performance of a frontier model—entirely on-premises? We’ll share our journey building a relevance classifier for agentic recommendations, covering fine-tuning, distillation, quantization, and inference optimization, along with the wins, dead ends, and hard numbers.

​Resources and Legal

• ​Resources

​NVIDIA Privacy Policy: https://www.nvidia.com/en-us/about-nvidia/privacy-policy/

Criteo Privacy Policy: https://www.criteo.com/privacy/