Boost Machine Learning Inference with Cost-Effective AWS Graviton3-backed EC2 C7g Instances

Boost Machine Learning Inference with Cost-Effective AWS Graviton3-backed EC2 C7g Instances

Boost Machine Learning Inference with Cost-Effective AWS Graviton3-backed EC2 C7g Instances

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Introduction to AWS Graviton3-based Amazon EC2 C7g Instances

Machine learning (ML) has become essential for organizations aiming to extract valuable insights from their data, improve their products, and make better decisions. Amazon SageMaker makes it easier for data scientists, engineers, and professionals working in machine learning to build, train, and deploy ML models in the cloud. Alongside its comprehensive ML infrastructure, SageMaker provides various deployment options and integrates with MLOps tools to streamline operations. One of the critical components in maximizing ML performance is choosing the right compute instances for inference tasks, and this is where AWS Graviton3-based EC2 C7g instances come in.

Benefits of AWS Graviton3-based Instances

Offering up to 50% cost reduction to comparable EC2 instances, AWS Graviton3-based Amazon EC2 C7g instances provide an exceptional alternative for those looking to reduce machine learning inference costs. These instances enable improved latency and performance, along with support for popular ML frameworks like PyTorch, TensorFlow, XGBoost, and scikit-learn. As a result, businesses can enjoy significant savings while optimizing their ML models’ performance on Amazon SageMaker.

Benchmarking Results

To demonstrate the benefits of using EC2 C7g instances, let’s compare their performance against c6g.4xlarge, c6i.4xlarge, and c5.4xlarge instances. The results reveal a clear cost advantage for the Graviton3-based instances across PyTorch, TensorFlow, XGBoost, and scikit-learn models. Moreover, these instances also offer a significant improvement in latency, allowing for better real-time ML model performance.

Migrating to AWS Graviton Instances

If you’re considering migrating to AWS Graviton3-based instances, there are two main deployment options available: using AWS Deep Learning Containers (DLCs) or bringing your own compatible containers. To leverage an ARM-based DLC for your SageMaker PyTorch estimator, follow these steps:

  1. Retrieve the ARM-based Docker image URI from the Deep Learning Container Images page.
  2. Utilize the image URI in your SageMaker PyTorch estimator by specifying the image_name parameter.
  3. Adjust the instance type to use Graviton3-based instances (e.g., c7g.xlarge) in the SageMaker estimator configuration.

Once you have updated your estimator, you can create and deploy models with ARM-based containers and AWS Graviton instances to maximize your machine learning inference performance.


AWS Graviton3-based EC2 C7g instances offer numerous advantages for businesses looking to optimize their machine learning inference costs and performance. With up to 50% cost savings and improved latency, these instances provide the perfect balance between affordability and efficiency. By migrating to Graviton3-based instances and integrating them with Amazon SageMaker, organizations can continue to leverage the power of ML while significantly reducing their operational costs. Don’t hesitate to evaluate and consider migrating your machine learning workloads to AWS Graviton instances for unmatched performance improvements.

Casey Jones Avatar
Casey Jones
1 year ago

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