Testimonial – WhiteFiber
How did WhiteFiber converge their GPU and storage workloads onto one fabric with DriveNets fabric scheduled Ethernet
WhiteFiber CTO, Tom Sanfilippo, explains how one of the biggest challenges at WhiteFiber, an AI cloud company, has been building GPU clusters around Ethernet technology, but without the potential bottlenecks of mixing workloads from multiple customers simultaneously. Watch the full interview and read the transcript below to learn how DriveNets’ fabric scheduled Ethernet has allowed WhiteFiber to converge our GPU and storage workloads onto one fabric.
Full Transcript
Hi, I’m Tom Sanfilippo. I’m the CTO at WhiteFiber.
We’re an AI cloud company focused on performance and scalability for AI applications of any size.
One of our biggest challenges has been building GPU clusters around Ethernet technology, but without the potential bottlenecks of mixing workloads from multiple customers simultaneously. In the past, we would build GPU clusters using separate networks for storage and compute. This makes it challenging to build very large networks. It increases the network complexity. It increases the number of components we have to use.
DriveNets’ fabric scheduled Ethernet has allowed us to converge our GPU and storage workloads onto one fabric. This is something we couldn’t do with other technologies. It’s provided great efficiency in terms of network throughput. It enables us to support multi-tenancy better by providing isolation and fair queuing across the entire network infrastructure.
We are looking to expand clusters using the DriveNets technology and apply it in new areas as well, including cross data center connectivity, cluster to cluster connectivity and building larger superclusters.
The DriveNets Infrastructure Services team was also a really big help in getting our POC built rapidly and proving out the technology for us to make the decision to move to the next step and bring that environment into production. They did an amazing job. It’s been a real pleasure working with them.
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