Hewlett-Packard Enterprise (HPE) today extended its server portfolio to address a broader range of use cases involving both edge computing applications and disconnected environments.
Scheduled to be made available later this year, a ruggedized HPE ProLiant Compute EL2000 chassis is now providing the foundation for two additional Gen12 servers that are based on Intel Xeon 6 processors. It is designed to deploy up to two HPE ProLiant Compute EL220 Gen12 servers or one EL240 Gen12 server that can scale from eight to 144 cores. Additionally, IT teams can configure the EL240 Gen12 server with NVIDIA RTX PRO 4500 or NVIDIA RTX PRO 6000 Blackwell Server Edition graphical processor units (GPUs).
An enhanced 2U HPE ProLiant DL145 Gen11 server, meanwhile, is based on AMD EPYC 8005 series processors, can be configured with up to 84 cores and will be available beginning in May. There is also a version of the HPE ProLiant DL145 Gen11 server that has been validated for AI inferencing applications at the network edge using the NVIDIA RTX PRO 4500 Blackwell Server Edition GPU and another version that has been certified to run Azure Local, an on-premises edition of the Microsoft cloud computing environment.
Finally, each platform is now available with an Environmental Ruggedization Option Kit ideal for harsh locations, including high- or low-altitudes, extreme temperatures, and hazardous transit.
Krista Satterthwaite, senior vice president and general manager for compute at HPE, said that as IT continues to evolve, especially in the age of artificial intelligence (AI), it’s clear more data than ever will be processed at the network edge. There is simply too much data being generated that needs to be analyzed in near real-time, which, because of latency concerns, will preclude sending that data to the cloud to be processed, she added.
There is, of course, a certain amount of irony in that shift. For the past decade, many IT teams have been focused on moving as much data as possible into the cloud. However, in the AI era many of them are finding there is once again a pressing need to bring the compute needed to wherever data is being generated and stored. Given the fact that more data than ever will be generated and analyzed at the network edge, the need to deploy AI inference engines as close as possible to the point where AI data is being consumed is driving more IT teams toward an IT architecture that is based on hybrid cloud computing.
Regardless of which type of application workload is deployed where, the one thing that is clear is that IT environments need to be more fluid. An application workload that has been deployed in one environment may need, for any number of reasons, to be moved to another platform. The challenge, as always, is to make sure that the right IT infrastructure is always available to optimally run a workload no matter where and how it may have been originally deployed.
