July 13, 2022 — Over the last decade, the ever-increasing demand for compute capacity and digital transformation efforts has accelerated global spending on public cloud services. Cloud Service Providers have responded to this demand with a massive build out of datacenters and a broad portfolio of cloud services, making the cloud the preferred choice for businesses to scale-out applications.
However, with increased focus on global sustainability and ESG goals, the energy efficiency of these infrastructure solutions has become just as important to cloud providers as performance and security. Ampere Altra Cloud Native Processors are the ideal CPUs to build out the high performance and high efficiency cloud. Building out this infrastructure with inherently Cloud Native technology delivers predictable and scalable performance to a wide variety of cloud service providers, digital service providers and digital-first enterprises.
Earlier today, as web Google Cloud announced a new class of Ampere Cloud Native Processor based Tau VMs to address these specific needs of the modern cloud applications. The Tau T2A VMs are designed to run scale-out Cloud Native workloads such and application servers, databases, Artificial Intelligence, video processing and more in an efficient manner.
The Tau T2A VMs are designed from the ground up for predictable high performance and linear scalability using single-threaded cores, eliminating a range of noisy neighbor concerns. They are available in various configurations with up to 48 vCPUs.
Compelling Performance with Superior Energy Efficiency
Ampere Altra Cloud Native Processors lead in performance for popular Cloud Native applications while running at lower power than legacy x86 processors. In Google Cloud, the T2A VM instances outperform current generation x86 VMs by up to 31% and lead on price-performance by up to 65% using on-demand pricing guidance*.
Giving Developers What They Want
The Arm-based server ecosystem has rapidly matured over the last few years with open-source Cloud Native software stacks extensively tested and deployed on Ampere Altra-based servers. For example, Ampere runs over 135 popular applications across 5 different cloud native infrastructures to ensure that our customers have confidence in the Ampere software environment across the marketplace. Our Solutions site features the results of this test regression suite and now contains daily results from Google Cloud Tau T2A as well. The T2A VMs support the most popular Linux operating systems like RHEL, CentOS Stream, Ubuntu, and Rocky Linux. Ampere, in partnership with Google Cloud has spent time working with Enterprise and Open-Source Linux OS vendors and communities to ensure these operating systems are optimized for the Ampere Cloud Native Processors. Both Canonical’s Ubuntu and Red Hat’s RHEL OSes have previously been certified for Ampere’s reference platforms. Additionally, Ampere has worked with the opensource Enterprise Linux teams within the Centos Stream and Rocky Linux communities to ensure both are also extensively tested and provide the best possible experience on Ampere Altra-based VMs.
The Google Cloud Tau Ampere Altra-based T2A VMs are currently in preview in several Google Cloud regions – us-central (Iowa – Zone A,B,F), europe-west4 (Netherlands – Zone A,B,C) and asia-southeast1 (Singapore – Zone B,C) and will be generally available in the coming months. To get started, go to the Google Cloud Console and select T2A for your VMs.
With Ampere, the processor, servers, software, and services are all Cloud Native. It’s time to move from legacy to a top-to-bottom cloud architecture. Choice is here, it’s compelling, and it’s good for cloud developers.
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* Based on est. SPEC CPU2017 Integer Rate Estimated for n2-standard-32, t2a-standard-32 VMs running on Debian 11 and compiled with gcc 10.2.1, -O3, -mcpu=native JEMalloc 5.2.1. Price performance was calculated from Google Cloud VM Instance Pricing for T2A-32 (Ampere Altra) VMs July, 2022. Memory and storage is same across all the VM’s, and hence not included.
Source: Ampere Computing