As it aims to meet increasing service provider and enterprise demand for model training and inferencing workloads on networks in its artificial intelligence (AI) datacentres, Vultr is to implement the AMD Helios AI Rack by HPE at a number of the cloud infrastructure company’s locations in the US.
The deal between the two companies is valued at 1.2bn and is HPE’s first order for the new AMD Helios system, which features purpose-built networking scale-up switching and software.
Founded in 2014, Vultr’s stated mission is to empower developers and businesses by simplifying the deployment of infrastructure via its cloud platform. The company is strategically located in 33 global cloud datacentre regions in 150+ countries and provides provisioning of public cloud, storage and single-tenant bare metal.
Vultr stresses that it has made it a priority to offer a standardised high-performance cloud compute environment in all of the cities it serves. It has over 75 engineers and developers, and has spun up more than 80 million cloud servers since 2014 for more than 1.5 million customers answering over 35,000 support requests per month.
In the deployment, HPE and the processor giant will support Vultr’s intention to meet service provider and enterprise demand for model training and inferencing workloads in its AI datacentres.
The AMD Helios AI Rack is part of the HPE AI datacentre solutions portfolio, a deeply integrated, production-ready infrastructure stack with AMD that is designed using open standards and is said to be optimised for the economics and scale of the AI lifecycle.
AMD designed Helios as a high-density system for trillion-parameter model training and high-volume inference. Each rack is built to integrate 72 AMD Instinct MI455X GPUs with AMD EPYC Venice CPUs, AMD Pensando Vulcano AI NICs networking and AMD ROCm software. The unified design is claimed to be optimised for power, cooling and serviceability, while supporting open, interoperable rack-scale fabrics such as UALink over Ethernet (UALoE).
Helios also features purpose-built HPE networking capability including six HPE Juniper Networking QFX5252 scale-up Ethernet switch trays per rack to connect all 72 GPUs with high-bandwidth, low-latency, standards-based Ethernet. HPE services reduces project and operational risk through expertise in end-to-end deployment and liquid-cooling for large AI clusters.
Furthermore, HPE believes that the technology will offer Vultr a tangible example of its integrated strategy that combines HPE Juniper Networking technologies with HPE compute, exascale liquid cooling, and rack-scale engineering to deliver an integrated, open Ethernet-based scale-up AI solution.
The technology installation also forms part of HPE’s self-driving network strategy and adoption of quantum technologies to address the way in which networking workloads are being driven by both users and AI agents with the latter potentially able to fundamentally transform how businesses design and build networks.
Speaking in June 2026 about this change, HPE president and CEO Antonio Neri stressed the importance of getting AI integration right with a comparison to network architecture construction: “You don’t design a building around a single room, you design a structure that allows the whole system to flow and develop over time. Architecting for AI demands the same focus and discipline. Fundamentally, AI is only as strong as the data foundation beneath it. If the foundation is not robust, nothing else holds.”
Addressing the new deployment, Neri said: “AI is driving the largest infrastructure buildout in history and together with AMD and Vultr, we are building an open foundation to accelerate the next phase of AI. AMD Helios by HPE demonstrates HPE’s unique ability to engineer the complete AI architecture with rack-scale compute, scale-up networking and direct liquid cooling, all delivered by our global services.”
Vultr CEO J J Kardwell added: “Demand for high-performance AI infrastructure continues to outpace available capacity, and our work with AMD and HPE enables us to bring Helios rack-scale compute and scale-up networking online for customers faster.
“Together, we’re delivering the memory capacity, interconnect performance, and energy efficiency customers need to run the largest and most demanding AI training and inference workloads.”
