HPE + AMD Launch “Helios”: A Game-Changer for AI & Supercomputing Infrastructure

In December 2025, HPE announced that it will be among the first to adopt AMD’s new “Helios” rack-scale AI architecture, a powerful, open-standard platform designed to handle large-scale AI training and inference workloads. 

What Is Helios And What Makes It Special

• Massive GPU density per rack - A single Helios rack can house up to 72 AMD Instinct MI455X GPUs, delivering unprecedented compute power for demanding AI and HPC workloads. 

• Staggering performance metrics - Helios offers up to 2.9 AI exaFLOPS (FP4) per rack, with support for training and inference at scales relevant for very large AI models. 

• High-bandwidth memory & data throughput - The architecture includes 31 TB of HBM4 memory and delivers 260 TB/s aggregated scale-up bandwidth, enabling high-speed data movement required by modern AI workloads. 

• Open-standard design for future-proofing - Helios is built on standards from the Open Compute Project (OCP) and uses Ethernet-based “Ultra Accelerator Link over Ethernet (UALoE)” networking. That means greater flexibility, reduced vendor lock-in, and easier adoption across diverse data-center environments. 

• Turnkey solution with networking & cooling integration - HPE will offer Helios as a complete rack-scale system with purpose-built networking (via its Juniper Networking switches, developed in collaboration with Broadcom) and advanced liquid-cooling infrastructure, simplifying deployment for cloud service providers (CSPs), data centers, and AI labs. 

Why This Matters for AI, HPC & Cloud Providers

• Scales AI infrastructure for modern model demands - With model sizes and data needs growing rapidly, Helios offers the compute, memory, and bandwidth required for “trillion-parameter models,” large-scale training, and high-throughput inference. 

• Accelerates time-to-deployment - As a turnkey rack solution with integrated networking and cooling, Helios can significantly reduce the complexity and deployment time of large AI clusters. 

• Promotes open standards - avoids vendor lock-in - By using OCP and Ethernet-based standards, Helios encourages interoperability, customization, and flexibility, important in a rapidly evolving AI infrastructure ecosystem. 

• Supports future HPC and AI workloads (cloud, research, enterprise) - Whether for supercomputing centers, AI-powered cloud services, or enterprise AI stacks, Helios offers a broadly applicable platform that can adapt to various workloads and scales. 

 Not All Use Cases Fit

• Helios is for large-scale deployments - This rack-scale architecture is intended for data centers, cloud providers, or research institutions, not for small offices or individual users.

• Requires significant infrastructure investment - To leverage full potential, you need compatible data-center facilities: power delivery, cooling, networking, and maintenance capacity.

• Specialized use cases - Helios excels at AI training, inference, HPC and large workloads, casual or consumer-level tasks won’t utilize (or require) this level of compute.

What’s Next

HPE plans to offer the Helios solution globally starting in 2026. 
For organizations anticipating growth in AI, HPC, or cloud services, Helios could mark a turning point, enabling deployment of cutting-edge compute infrastructure without being locked into proprietary hardware ecosystems.

If you’re managing or building out an AI data center, cloud offering, or HPC facility, it may be timely to start evaluating Helios as part of your 2026-onwards roadmap.