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Here's my conversation with Philip Kiely. A deep dive into inference engineering, his book his available online for free, and it's worth reading. Inference is becoming more important to people and organisations by the day, learning about the topic will prepare you well.

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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

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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Jesse Pollak on the ecosystem forming around tokenized inference "There's this really cool tokenized inference market that Venice has innovated, where they've taken inference and turned it into a token called DIEM where you can buy it and you get $1 of inference per day" "That innovation of tokenizing inference has unlocked a ton of innovation around it, where people are using that tokenized inference to do things" "There's other projects that let people sell their tokenized inference at a discount so other people can get cheaper inference and the whole system works more efficiently" "I feel like that's the first time we're really seeing the intersection of AI plus markets plus crypto and it's thanks to Venice and it's happening on Base"

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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 to unlock more throughput, why inference teams are still finding 20–200% performance gains, how video generation runs into a quadratic attention wall, and how GLM-5.2 helped rewrite and optimize the GPU kernels serving GLM-5.2 itself.

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$SNDK CEO David Goeckeler says inference is a memory-bound problem and HBF could break through some bottlenecks, so it's an enormous opportunity as inference scales, which they'll talk more at their analyst day next week. "Look, there's a ton of innovation going on right now in inference memory architectures, which we think is fantastic, very healthy." "There's a lot of different ideas. We're going to dive into this a little deeper next week at our analyst day…" "But yes, all these are opportunities for us. We think especially inference is a memory-bound problem. Storage is extraordinarily important to the equation." "We showed some stuff in our FMS keynote just a couple of hours ago about how, when you use HBF, we simulate performance and maybe break through some bottlenecks…" "As we scale inference, we think it provides an enormous opportunity. The way we're thinking about this is staying very close to our customers, because they're going to be the ones that define what the architecture is in the future."

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This is why Nvidia bought Groq. If Groq deploys a ton of low-cost inference chips, demand for training will increase massively. The more inference you have, the more training you need, and vice versa. Nvidia can keep its high margins selling chips for training forever.

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