 The recent surge in ARM processor capabilities has sparked a wave of exploration beyond their traditional mobile device domain. This blog explains why you may want to consider using ARM nodes for your Kubernetes workloads. We'll identify potential benefits of leveraging ARM nodes for containerized deployments while acknowledging the inherent trade-offs and scenarios where x86-64 architectures may perform better and thus continue to be a better fit. Lastly we'll describe a seamless way to add ARM nodes to your Kubernetes clusters. In this blog, for the sake of clarity and brevity, I will be using the term 'ARM' to refer to ARM64 or ARM 64-bit processors, while 'x86' or 'x86-64' will be used interchangeably to denote Intel or AMD 64-bit processors. What Kubernetes Workloads Tend To Be Ideal for ARM Processors? Inference-heavy tasks:While the computations involved in Deep Learning training typically require GPUs for acceptable performance, DL inference is less computationally intense. Tasks that apply pre-trained models for DL regression or classification can benefit from ARM's power/performance relative to GPU or x86-64 systems. We presented data on running inference on ARM64 in our Scale20x talk.
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