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The Inference Engineering Masterclass: 10x faster models, quantization, speculative decoding, Rubin, & self-optimizing AI Baseten Philip Kiely and ali explain what actually happens after a model is trained, why turning weights into a fast and reliable product creates an entirely new optimization problem, how quantization errors can cancel out... show more

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149,249 Aufrufe • vor 3 Monaten •via X (Twitter)

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Profilbild von John

Johnvor 3 Monaten

speculative decoding was the one that surprised me most in practice. the draft model rejection rate changes a lot based on prompt style, so batching similar prompt types together before running speculative decode gave a much bigger throughput lift than just tuning the draft model itself

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Aayaan Naqvivor 3 Monaten

@baseten @philipkiely @waterloo_intern woahhhhh @waterloo_intern sick

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Jong Hyun Parkvor 3 Monaten

@baseten @philipkiely @waterloo_intern Is a durable moat possible in inference tech on a multi year timescale?

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What is AI inference engineering, why is it such an in-demand skill, and how do you break into the field? With author of Inference Engineering Philip Kiely and head of training at Baseten Charlie O'Neill 0:00: What is inference? 2:47: History of inference 4:59: Downstream effects of AI research on inference 13:54: What you'll learn from Inference Engineering 16:14: Advice for engineers transitioning into AI 19:00: Open source models driving inference growth 20:55: Specialization vs. frontier closed models 23:51: "Big Token" and the importance of open source AI 27:18: Where to get Inference Engineering

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