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Perplexity AI: Under the Hood of LLM Inference

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If you have a question about this talk, please contact Ben Karniely .

Abstract: Perplexity is a search and answer engine which leverages LLMs to provide high-quality citation-backed answers. The AI Inference team within the company is responsible for serving the models behind the product, ranging from single-GPU embedding models to multi-node sparse Mixture-of-Experts language models. This talk provides more insight into the in-house runtime behind inference at Perplexity, with a particular focus on efficiently serving some of the largest available open-source models.

Biography:Nandor Licker is an AI Inference Engineer at Perplexity, focusing on LLM runtime implementation and GPU performance optimization.

Register for the talk at the following link: https://luma.com/dx1ggxgk

Some catering will be provided after the talk.

This talk is part of the Technical Talks - Department of Computer Science and Technology series.

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Cambridge Union, Kennedy Room Room GS15 at the William Gates Building and on Zoom: https://cl-cam-ac-uk.zoom.us/j/4361570789?pwd=Nkl2T3ZLaTZwRm05bzRTOUUxY3Q4QT09&from=addon Zoom Meeting ID: 928 9045 3974 Passcode: 313781

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