- HPE to Ship 72 GPU Racks Equipped with Next-Generation AMD Instinct Accelerators Worldwide
- Venice processors paired with GPUs target exascale-level AI performance per rack
- Helios relies on liquid cooling and a double-width chassis for thermal management
HPE has announced plans to integrate AMD’s Helios rack-scale AI architecture into its product line starting in 2026.
This collaboration gives Helios its first major OEM partner and allows HPE to deliver full 72-GPU AI racks built around AMD’s next-generation Instinct MI455X accelerators.
These racks will pair with EPYC Venice processors and use a scalable Ethernet-based fabric developed with Broadcom.
Rack layout and performance objectives
The move creates a clear commercial path for Helios and puts the architecture in direct competition with Nvidia’s already fielded rack platforms.
The Helios reference design is based on Meta’s Open Rack Wide standard.
It uses a double-wide, liquid-cooled chassis to house MI450 series GPUs, Venice processors, and Pensando networking hardware.
AMD is targeting up to 2.9 exaFLOPS of FP4 compute per rack with the MI455X generation, along with 31TB of HBM4 memory.
The system presents each GPU as part of a single pod, allowing workloads to span all accelerators without local bottlenecks.
A purpose-built HPE Juniper switch supporting Ultra Accelerator Link over Ethernet provides the high-bandwidth GPU interconnect.
It offers an alternative to Nvidia’s NVLink-centric approach.
The Stuttgart High Performance Computing Center has selected HPE’s Cray GX5000 platform for its next flagship system, named Herder.
Herder will use MI430X GPUs and Venice processors on directly liquid-cooled blades and will replace the current Hunter system in 2027.
HPE said waste heat from GX5000 racks would warm campus buildings, reflecting environmental considerations in addition to performance goals.
AMD and HPE plan to make Helios-based systems available globally next year, expanding access to rack-scale AI hardware for research institutions and enterprises.
Helios uses an Ethernet framework to connect GPUs and CPUs, which contrasts with Nvidia’s NVLink approach.
Using Ultra Accelerator Link over Ethernet and Ultra Ethernet Consortium-aligned hardware supports scalable designs within an open standards framework.
While this approach allows GPU counts theoretically comparable to other high-end AI racks, performance under sustained multi-node workloads remains untested.
However, relying on a single Ethernet layer could introduce latency or bandwidth constraints in real-world applications.
That said, these specifications do not predict actual performance, which will depend on effective cooling, network traffic management, and software optimization.
Via Tom’s material
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