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Google Axion Processors | Google Cloud

Learn about Titanium, a system of purpose-built chips with multiple tiers of data center wide scale-out offloads.

Leading processors custom-built for cloud workloads

These Arm-based CPUs offer best price-performance and energy efficiency for general purpose workloads, plus industry-leading performance per vCPU.

Overview

Why Axion processors?

Axion is a family of custom Arm-based processors for general purpose computing, powering the C4A instance family that delivers up to 10% better performance-per-vCPU than the latest Arm-based instances available in the cloud. Axion processors are the latest innovation in a long line of custom Google silicon, from Tensor Processing Units for AI, to Video Coding Units for YouTube, and Tensor chips for Pixel devices. Each of these significantly improved performance and efficiency for resource-intensive applications used by businesses and consumers everywhere.

Cost efficient

Get up to 65% better price-performance than other instances available on Google Cloud for a range of general purpose workloads like Java and MySQL. You can also use C4A with Cloud SQL and AlloyDB for PostgreSQL managed databases, now available in Preview, providing significant price-performance advantages for database workloads.

Energy efficient

Google Cloud data centers are 1.5X more efficient than the industry-average. Axion reduces consumption even more, using up to 60% less energy than other CPUs on Google Cloud.

Built on Titanium

Underpinning every Axion instance is Titanium, a system of purpose-built microcontrollers and tiered scale-out offloads to improve your infrastructure performance, life cycle management, and security.

Instances powered by Axion processors

Google Axion Processor Workloads Shapes

C4A

Web and app servers (high traffic)
Ad servers
Game servers
Data analytics
Databases (any size)
In-memory caches
Media streaming and transcoding
CPU-based AI/ML
Network appliances
Processors for cloud workloads

Up to 72 vCPUs, 576 GB RAM, and 100 Gbps networking.

Configurations:

Standard: 1:4 vCPU to memory
High memory: 1:8 vCPU to memory
High CPU: 1:2 vCPU to memory
Standard and High-memory configurations also offer Titanium SSD options with up to 6 TB in local storage capacity.

Workloads

Web and app servers (high traffic)
Ad servers
Game servers
Data analytics
Databases (any size)
In-memory caches
Media streaming and transcoding
CPU-based AI/ML
Network appliances
Processors for cloud workloads

Shapes

Up to 72 vCPUs, 576 GB RAM, and 100 Gbps networking.

Configurations:

Standard: 1:4 vCPU to memory
High memory: 1:8 vCPU to memory
High CPU: 1:2 vCPU to memory
Standard and High-memory configurations also offer Titanium SSD options with up to 6 TB in local storage capacity.

How It Works

Learn about seven possible migration paths from x86 or other Arm-based CPUs to Axion, from Google managed services to containerized workloads, Linux-interpreted languages (Java, Python, PHP, Ruby), and more.

7 ways to migrate to Arm-based CPUs on Google Cloud

Common Uses

Create your first VM How-tos

Three ways to get started

Documentation: Creating an Arm VM instance How-tos

Three ways to get started

Documentation: Creating an Arm VM instance Deploy a Java application How-tos

Tutorial

Want to learn how to run Java-based applications on Axion? Most applications will run with no changes needed, but there are best practices and optimizations that can help improve your performance.

Take this Arm ‘Learning Path’ to:

Understand Arm performance for different JDK versions
Test common performance optimization flags

How-tos

Tutorial

Want to learn how to run Java-based applications on Axion? Most applications will run with no changes needed, but there are best practices and optimizations that can help improve your performance.

Take this Arm ‘Learning Path’ to:

Understand Arm performance for different JDK versions
Test common performance optimization flags

Build a multi-arch CI/CD pipeline How-tos

Tutorial

Creating a multi-architecture pipeline helps you build applications across multiple platforms with more flexibility, speed, and efficiency.

Take this Arm ‘Learning Path’ to:

Create an Axion-based GitLab self-hosted runner
Build a CI/CD pipeline with multi-architecture support
Learn best practices for building multi-architecture docker images using native GitLab runners
Build and test a multi-arch application with example code

How-tos

Tutorial

Creating a multi-architecture pipeline helps you build applications across multiple platforms with more flexibility, speed, and efficiency.

Take this Arm ‘Learning Path’ to:

Create an Axion-based GitLab self-hosted runner
Build a CI/CD pipeline with multi-architecture support
Learn best practices for building multi-architecture docker images using native GitLab runners
Build and test a multi-arch application with example code

Build a multi-arch application on GKE How-tos Additional resources

Tutorial

To build a multi-arch application or migrate your existing containerized applications running on Google Kubernetes Engine (‘GKE’) to Axion, start by reading our documentation.

Need step-by-step instructions? Take this Arm ‘Learning Path’ to:

Learn how to add Axion nodes to an existing x86-based GKE cluster, or rebuild x86-based applications to run on Axion
Learn how to add taints and tolerations to GKE clusters to schedule application pods on architecture-specific nodes