Reiner Pope – The math behind how LLMs are trained and served

Deskripsi

Did a very different format with Reiner Pope - a blackboard lecture where he walks through how frontier LLMs are trained and served.It’s shocking how much you can deduce about what the labs are doing from a handful of equations, public API prices, and some chalk.It’s a bit technical, but I encourage you to hang in there – it’s really worth it.There are less than a handful of people who understand the full stack of AI, from hardware design to model architecture as well as Reiner. It was a real delight to learn from him.Recommend watching this one on YouTube so you can see the chalkboard.Reiner is CEO of MatX, a new chip startup (full disclosure - I’m an angel investor). He was previously at Google, where he worked on software efficiency, compilers, and TPU architecture.Download markdown of transcript here to chat with an LLM - working on some flashcards to help us all retain the content in this episode - come back here in a few hours!Sponsors* Jane Street needs constant access to incredibly low-latency compute. I recently asked one of their engineers, Clark, to talk me through how they meet these demands. Our conversation—which touched on everything from FPGAs to liquid cooling—was extremely helpful as I prepped to interview Reiner. You can watch the full discussion and explore Jane Street’s open roles at janestreet.com/dwarkesh* Google’s Gemma 4 is the first open model that’s let me shut off the internet and create a fully disconnected “focus machine”. This is because Gemma is small enough to run on my laptop, but powerful enough to actually be useful. So, to prep for this interview, I downloaded Reiner’s scaling book, disconnected from wifi, and used Gemma to help me break down the material. Check it out at goo.gle/Gemma4* Cursor helped me turn some notes I took on how gradients flow during large-scale pretraining into a great animation. At first, I wasn’t sure the best way to visualize the concept, but Cursor’s Composer 2 Fast model let me iterate on different ideas almost instantaneously. You can check out the