# High-Performance Compute on Autopilot


This week, Autopilot announced support for the [Scale-Out Compute Class](https://cloud.google.com/blog/products/containers-kubernetes/deploying-arm-workloads-on-gke-autopilot-with-the-scale-out-compute-class), for both x86 and Arm architectures. The point of this compute class is to give you cores for better single-threaded performance, and improved price/performance for “scale-out” workloads — basically for when you are saturating the CPU, and/or need faster single-threaded performance (e.g. remote compilation, etc).

To use, simply add “compute-class: Scale-Out” to your workloads. They can be Arm, or x86 — but pay attention to the available regions. Also note that you need to be using a very new version of Autopilot (see this blog for a CLI command to get you a Scale-Out qualified cluster).

As can be seen by inspecting the nodes with `kubectl describe`, this compute class is currently served by the T2D and T2A GCE VM types, so you can review those docs for the performance characteristics of those machines. Regarding [regional availability](https://cloud.google.com/kubernetes-engine/docs/concepts/autopilot-compute-classes#availability), x86 (T2D) is available in 13 regions, while Arm (T2A) is in 3.

With this launch, Autopilot moves closer to the goal of offering 100% workload compatibility for non-administrative workloads running on GKE. We’re not building a toy Kubernetes environment here, but one that is fully featured to run anything you can throw at it. This wouldn’t be possible with a single, “flat” compute offering, as not all workloads share the same compute requirements. Now you have a great home for your CPU-intensive batch jobs, and (thanks to Autopilot’s support for StatefulSet with block storage) higher-performance databases.