Inside Google's TPU: From Systolic Arrays to Light-Powered AI Supercomputers

Why did one chip change the course of modern AI? This video walks through how Google moved from a performance plateau into a new era by sacrificing generality for extreme specialization, building the TPU family to power inference and training at massive scale. You will see how the TPU V1 used a systolic array to make matrix math blisteringly efficient, how TPU V2 added programmability and a 2D torus interchip network to enable distributed training, and how TPU4 shattered data center limits with an optical circuit switch that can link 4,096 chips while using under 5% of system power. The real lesson is that hardware alone did not win this race, it was hardware and software co-designed from silicon to cluster orchestration.

• The Moore’s Law slowdown forced specialization, not just faster general-purpose CPUs.
• TPU V1 was purpose-built for inference, using systolic arrays to maximize math throughput.
• TPU V2 introduced modular, programmable cores and a high-speed interchip interconnect for large-scale training.
• TPU4 replaced rigid copper grids with an optical circuit switch, enabling huge, reconfigurable pods of 4,096 chips and dramatic power efficiency.
• The invisible software stack, including Borg, XLA, and SPMD, is what makes thousands of chips behave like one massive virtual processor.

If you found this useful, like and subscribe for more deep dives on the tech behind AI. Tell me in the comments which TPU innovation surprised you most, or what hardware+software combo you think will come next. This video has been generated automatically by AutoContent API - https://autocontentapi.com