Google Selects AMD for Tau VMs
June 18, 2021
and Google Cloud introduced T2D, the first instance in the new family of
Tau Virtual Machines (VMs) powered by 3rd Gen AMD EPYC processors.
According to Google Cloud, the T2D instance offers 56% higher absolute
performance and more than 40% higher price performance for scale-out
The Tau VM family provides customers with a leading combination of
performance, price, and easy integration. The T2D instances, using the
leadership performance of 3rd Gen AMD EPYC processors, excels at
workloads including web servers, containerized microservices, data
logging-processing, large scale Java® applications and more.
“At Google Cloud, our customers’ compute needs are evolving,” said
Thomas Kurian, CEO of Google Cloud. “By collaborating with AMD, Google
Cloud customers can now leverage amazing performance for scale-out
applications, with great price-performance, all without compromising x86
designed 3rd Gen AMD EPYC processors to meet the growing demand from
cloud and enterprise customers for high-performance, cost-effective
solutions with optimal TCO,” said AMD President and CEO Dr. Lisa Su. “We
work closely with Google Cloud and are proud they selected AMD to
exclusively power the new Tau VM T2D instance which provides customers
with powerful new options to run their most demanding scale-out
The industry-leading2 3rd Gen AMD EPYC processors allow Google Cloud
customers to seamlessly integrate workloads with their existing x86
ecosystems, enabling applications and frameworks to work with the T2D
instances. The new instances are offered in eight different predefined
VM shapes, with up to 60 vCPUs per VM, and up to 4GB of memory per vCPU,
making this technology ideal for scale-out workloads.
AMD EPYC processors power numerous instances at Google Cloud that
support workloads including compute optimized, general purpose,
high-performance and confidential computing. These instances are used by
well-known cloud-native companies, spanning multiple industries,
providing high-performance cloud instances for their workloads.