A key benefit of using general-purpose processors to implement open RAN/vRAN is that the same platforms can be used to support AI inference and other applications at the far edge of the network, such as cell site routers (CSRs) and content delivery and hosting. These edge platforms can be used to host virtualized applications closer to the user, offering significant benefits in terms of lower latency and shared infrastructure. To find out more about which applications service providers plan to support on shared far edge solutions and how they plan to deploy open RAN and vRAN platforms and architectures for 5G networks, Heavy Reading ran an exclusive survey of individuals working for operators with mobile network businesses. The results are presented in an analyst report, Open RAN Platforms and Architectures Operator Survey Report, that can be downloaded for free here. The survey presented options for five edge applications that can share server platforms with virtualized open RAN baseband implementations.

The growth of AI/Deep learning and data analytics has created many of the most challenging HPC workloads in recent years. The latest HPC report by Hyperion Research states that iterative simulation workloads and new workloads such as AI and other Big Data jobs would drive the adoption of HPC storage. To keep up with the growing massive amount of data we are collecting, users need to enhance computation performance at the same time and hence HPC requires equally robust storage to maintain compute performance for faster data in and out as we are heading into the Big Data era now. Data-intensive HPC is driving new storage requirements and making a change. For the simulation process, it not only requires a large amount of computations running on HPC infrastructures built on a cluster of powerful servers linked together with networking and memory, but also adds in self-service concept data stores.

Nvidia's latest cloud server platform is intended as a "building block," in the reference design sense, to support AI training and inference along with HPC workloads such as simulations. The GPU vendor (NASDAQ: NVDA) introduced its latest server platform dubbed HGX-2 on Wednesday (May 30) during a company roadshow in Taipei, Taiwan. Nvidia said the cloud server can be throttled up or down to support precision HPC calculations from 32-bits for single-precision floating point format, or FP32, up to double-precision FP64. Meanwhile, AI training and inference workloads are supported with FP16, or half precision, along with Int8 data. The combination is designed for varying processing requirements for a growing number of enterprise applications that combine AI with HPC, the company noted.