Reiner Pope – Chip design from the bottom up
New blackboard lecture with Reiner Pope: how do chips actually work - starting with basic logic gates, and working up to why GPUs, TPUs, FPGAs, and the human brain each look the way they do.
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.
Watch this one on YouTube so you can see the chalkboard. Read the transcript.
Sponsors
* Crusoe was one of only five GPU clouds that made the gold tier in SemiAnalysis' most recent ClusterMAX report. Gold-tier providers like Crusoe delivered 5-15% lower TCO than silver-tier clouds, even with identical GPU pricing. This is because optimizations like early fault detection and rapid node replacement don't necessarily show up in the sticker price, but still matter a ton in the real world. Learn more at crusoe.ai/dwarkesh
* Cursor is where I do most of my work—from reading research papers to visualizing technical concepts to coding up internal tools for the podcast. Most recently, I used it to build two different review interfaces for my essay contest, one that anonymizes submissions for scoring and another that lets me see applicants' essays next to their resumes and websites. Whatever you're working on, you should try doing it in Cursor. Get started at cursor.com/dwarkesh
* Jane Street let me ask Ron Minsky and Dan Pontecorvo, two senior Jane Streeters, a bunch of questions about how they use AI. We discussed everything from the types of models they're training to how they think about the future of trading to why they're more bullish than ever on hiring technical talent. You can watch the full conversation and learn more about their open positions at janestreet.com/dwarkesh
Timestamps
00:00:00 – Building a multiply-accumulate from logic gates
00:16:31 – Muxes and the cost of data movement
00:26:10 – How systolic arrays work
00:39:11 – Clock cycles and pipeline registers
00:51:51 – FPGAs vs ASICs
01:03:25 – Cache vs scratchpad
01:07:27 – Why CPU cores are much bigger than GPU cores
01:12:00 – Brains vs chips
01:15:33 – A GPU is just a bunch of tiny TPUs
Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe
Reiner Pope – The math behind how LLMs are trained and served
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 chip 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.
Wrote up some flashcards and practice problems to help myself retain what Reiner taught. Hope it's helpful to you too!
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 animation in my recent blog post. And if you have something to visualize yourself, go to cursor.com/dwarkesh
Timestamps
(00:00:00) – How batch size affects token cost and speed
(00:32:09) – How MoE models are laid out across GPU racks
(00:47:12) – How pipeline parallelism spreads model layers across racks
(01:03:37) – Why Ilya said, “As we now know, pipelining is not wise.”
(01:18:59) – Because of RL, models may be 100x over-trained beyond Chinchilla-optimal
(01:33:02) – Deducing long context memory costs from API pricing
(02:04:02) – Convergent evolution between neural nets and cryptography
Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe
Two Veteran Chip Builders Have a Plan to Take On Nvidia
When it comes to chips for artificial intelligence, obviously the name that automatically comes to mind is Nvidia. The company is making a fortune selling semiconductors used for hot AI applications like large language models, and stock investors have rewarded it handsomely for doing so. But of course, Nvidia's GPUs can be used for more than just AI. They're also used for video games, graphics, cryptocurrency mining and more. But a new startup called MatX is aiming to build the ultimate chip just for LLMs. Co-founders Reiner Pope and Mike Gunter spent years at Alphabet, which has its own internal semiconductor operations, and now they've stalked out on their own to create a new chip company from scratch. We talk about how they're going about their job, what it takes to actually design and build a chip, and what it will take to get customers to switch over from the industry leader.
For more episodes on this topic:
Coreweave's CSO on What It Really Takes to Build an AI Datacenter
Inside the Battle for Chips That Will Power Artificial Intelligence
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