Scaling Generative Models Globally with NVIDIA Triton & Sagemaker

by Enrico Rotundo , Associate Data Scientist

In an insightful session presented by Enrico Rotundo, we explore the innovative approach to scaling StableAudio globally. This presentation sheds light on the synergy between NVIDIA Triton and AWS SageMaker.

NVIDIA’s Triton Inference Server has emerged as one of the more capable inference containers, especially if you need tighter NVIDIA GPU integration. In this presentation Enrico Rotundo discusses how he leveraged AWS Sagemaker and Triton to scale inference on

You will learn some of the challenges that he faced during implementation and the patterns he used to deliver success.

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The following is an automated summary of the presentation.

Leveraging NVIDIA Triton for Optimal Performance

The journey begins with NVIDIA Triton, an open-source inference server that facilitates the deployment of AI models across diverse environments. Triton’s flexibility and efficiency in managing multiple models simultaneously streamline operations and enhance performance, proving crucial for scaling StableAudio. Key highlights include:

  • Model Optimization: Triton’s support for dynamic batching and model pipelines optimizes resource utilization, ensuring high throughput and low latency.
  • Versatile Deployment: With Triton, StableAudio is deployable across varied hardware, from fully managed cloud environments to on-premise GPUs, without compromising performance.

Challenges Encountered with NVIDIA Triton

  1. Model Compatibility and Optimization: Ensuring models are fully compatible with Triton’s requirements, and optimizing them for efficient deployment across various hardware setups.
  2. Dynamic Batching Configuration: Tuning dynamic batching settings for optimal performance without incurring excessive latency, especially for models with variable input sizes.
  3. Resource Management: Balancing the server’s resource allocation to handle simultaneous model inferences efficiently without overloading the system.

Enhancing Scalability with AWS SageMaker

Transitioning to AWS SageMaker, the presentation delves into how it simplifies the deployment and management of AI models. SageMaker’s comprehensive suite of tools and services automates crucial processes, enabling seamless scalability of StableAudio. Significant advantages include:

  • Autoscaling Deployment: SageMaker’s managed services reduce the complexity of deploying and managing AI models, allowing for efficient scaling.

Challenges Encountered with AWS SageMaker

  1. Integration Complexity: Integrating SageMaker seamlessly with existing CI/CD pipelines and development workflows, ensuring automated deployment and scaling.
  2. Cost Management: Monitoring and managing costs associated with using SageMaker’s managed services, especially when scaling up resources for large-scale deployments.


Through a blend of NVIDIA Triton’s inference serving capabilities and AWS SageMaker’s comprehensive deployment and management services, Winder.AI showcases a robust strategy for scaling StableAudio globally.

These challenges reflect the complexities of deploying and scaling AI models in a production environment, necessitating a thorough understanding of both NVIDIA Triton and AWS SageMaker’s capabilities and limitations. Addressing these challenges successfully can lead to significant improvements in model performance, scalability, and operational efficiency.

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