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The past few decades have seen almost unimaginable advances in compute performance and efficiency, enabled by Moore’s Law and underpinned by scale-out commodity hardware and loosely coupled software. This architecture has delivered online services to billions globally and put virtually all of human knowledge at our fingertips.
But the next computing revolution will demand much more. Fulfilling the promise of AI requires a step-change in capabilities far exceeding the advancements of the internet era. To achieve this, we as an industry must revisit some of the foundations that drove the previous transformation and innovate collectively to rethink the entire technology stack. Let’s explore the forces driving this upheaval and lay out what this architecture must look like.
From commodity hardware to specialized compute
For decades, the dominant trend in computing has been the democratization of compute through scale-out architectures built on nearly identical, commodity servers. This uniformity allowed for flexible workload placement and efficient resource utilization. The demands of gen AI, heavily reliant on predictable mathematical operations on massive datasets, are reversing this trend.
We are now witnessing a decisive shift towards specialized hardware — including ASICs, GPUs, and tensor processing units (TPUs) — that deliver orders of magnitude improvements in performance per dollar and per watt compared to general-purpose CPUs. This proliferation of domain-specific compute units, optimized for narrower tasks, will be critical to driving the continued rapid advances in AI.
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Beyond ethernet: The rise of specialized interconnects
These specialized systems will often require “all-to-all” communication, with terabit-per-second bandwidth and nanosecond latencies that approach local memory speeds. Today’s networks, largely based on commodity Ethernet switches and TCP/IP protocols, are ill-equipped to handle these extreme demands.
As a result, to scale gen AI workloads across vast clusters of specialized accelerators, we are seeing the rise of specialized interconnects, such as ICI for TPUs and NVLink for GPUs. These purpose-built networks prioritize direct memory-to-memory transfers and use dedicated hardware to speed information sharing among processors, effectively bypassing the overhead of traditional, layered networking stacks.
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