Stanford CS153 Frontier Systems | The Discipline of Delivering Value per Gigawatt

For more information about Stanford's online Artificial Intelligence programs, visit: https://stanford.io/ai Follow along with the course schedule and syllabus, visit: https://cs153.stanford.edu/ In a CS153 Frontier Systems lecture, the class returns to the upstream infrastructure stack with Amin Vahdat, who leads Google's internal compute infrastructure and the TPU program powering Gemini, framing his nearly 30-year career as the discipline of building reliable, balanced supercomputers at a planetary scale. Vahdat argues the industry is over-fixated on gigawatts and flops as headline metrics: at roughly $40 to $50 billion per gigawatt, the question that matters is value delivered per dollar, measured in happy daily active users and paying enterprise customers, not raw capacity. He walks through the three constraints that govern utility. Reliability, where moving from 99 percent to 99.9 percent uptime closes a 3.65-day annual gap, and where Frontier Labs are newly willing to trade five-nines for double the capacity. System balance, invoking Amdahl's 1967 law that every million instructions per second needs a megabyte per second of I/O, now stretched across 100,000-node synchronous training jobs where a single failed node halts the entire computation. And procurement lead times of two to three years for net-new gigawatts, where land permitting, utility contracts, and 20-year take-or-pay power agreements have replaced the slack capacity that once let hyperscalers ask for ten megawatts on a handshake. He details Google's optical circuit switch architecture, which uses 136 MEMS-controlled mirrors per chip to programmatically rewire the 3D torus topology connecting TPU racks, allowing failed racks to be virtually swapped in seconds and bandwidth redirected to distant storage clusters for the duration of a five-hour Borg job. Vahdat closes on responsibility: data centers should be a net uplift to local grids and communities, citing Google's choice to accept 10 percent worse power efficiency in water-scarce regions and its gigawatt-scale demand response program that returns capacity to utilities during peak residential load. Amin Vahdat is a Fellow and Chief Technologist for AI Infrastructure at Google, where his team is responsible for delivering industry-leading infrastructure which spans custom silicon, data centers, network, and supply chain and operations. This infrastructure serves Alphabet, Google and the world, and Artificial Intelligence technologies that empower ML developers and solve customers’ most pressing business challenges. In the past, he was Vice President and General Manager for Google's compute, storage, and network hardware and software infrastructure. Until 2019, he was the Technical Lead and Vice President for the Networking organization at Google. Before joining Google, Amin was the Science Applications International Corporation (SAIC) Professor of Computer Science and Engineering at UC San Diego (UCSD). He received his doctorate from the University of California Berkeley in computer science, and is a Fellow of the Association for Computing Machinery (ACM). Amin has been recognized with a number of awards, including the National Science Foundation (NSF) CAREER award, the UC Berkeley Distinguished EECS Alumni Award, the Alfred P. Sloan Fellowship, the Association for Computing Machinery's SIGCOMM Networking Systems Award, and the Duke University David and Janet Vaughn Teaching Award. Amin was awarded the SIGCOMM lifetime achievement award for his contributions to data center and wide area networks. He was inducted into the National Academy of Engineering in 2023 for his contributions to the design and implementation of datacenter and planet-scale networks that power cloud computer systems. Follow the playlist:    • Stanford CS153 Frontier Systems