Working PCIe 6.0 connectivity demoed at GTC — Astera Labs' Aries retimers currently power Nvidia's HGX systems with eight H100 GPUs, likely future Blackwell systems, too

Astera Labs
(Image credit: Tom's Hardware)

The GTC 2024 show floor was teeming with interesting tech demos as Nvidia's partners looked to capitalize on the glitz and glam of what will probably be the largest bevy of AI GPU announcements of the year. Astera Labs' demo of its working PCIe 6.0 connectivity with its new Aries retimers was among the most interesting — the company had a demo of a working PCIe 6.0 retimer that reduces the amount of energy required per bit to less than half that of the preceding generation.

The demo also gives us a compelling look at the nuts and bolts of the components required to assemble the world's fastest AI GPU clusters by increasing the reach of the PCIe 6.0 connection between next-gen GPUs and CPUs, like Nvidia's new Blackwell B100, B200, and GB200 silicon.

AI workloads require high levels of throughput between GPUs to process data at full speeds, which is why Nvidia has its custom NVLink and NVSwitch solutions to offer massive amounts of GPU-to-GPU communication, with up to 1.8 TB/s of bandwidth on GB200 systems like the GB200 NVL72. However, not everyone wants to go all-in with Nvidia, and a variety of partners still use PCIe-based GPUs.

Many of today's high-powered GPU clusters employ fast PCIe 5.0 x16 connections that deliver 128 GB/s of bidirectional bandwidth between the components to keep the GPUs as saturated as possible and utilize them to the fullest. The industry is now moving to the PCIe 6.0 interface to double bandwidth once again, reaching up to 256 GB/s of bandwidth on a x16 connection. This necessitates even faster supporting components to extend the reach of the interface, but performance-sapping bit error rates are becoming even more of a challenge with the new interface.

Paul Alcorn
Editor-in-Chief

Paul Alcorn is the Editor-in-Chief for Tom's Hardware US. He also writes news and reviews on CPUs, storage, and enterprise hardware.