GPU Cloud Computing
This flexible expansion platform enables users to add any PCIe Gen 4.0 application accelerators, including GPUs, FPGAs, IPUs, DPUs, thin-NVMe-servers and specialty AI chips.
GPUs are often Trapped Inside Servers, Limiting Utilization and Flexibility
GPUs are heavy in energy consumption — both to run and to keep cool — and yet, they’re still greatly underutilized. Underutilized GPUs needlessly waste energy and drive up operating costs.
The following chart from towardsdatascience.com depicts the average GPU utilization by user. Which shows a decrease in GPU utilization across a majority of those users.
“Nearly a third of our users are averaging less than 15% utilization. Average GPU memory usage is quite similar. Our users tend to be experienced deep learning practitioners and GPUs are an expensive resource so I was surprised to see such low average usage.” –Â Â towardsdatascience.com
Enter GigaIO’s FabreX Environment
FabreX Memory Fabric breaks the through the server chassis barrier and disaggregates rack components, like GPUs, into pools of compute resources, allowing for an increase in GPU utilization.Â
Related Resources
