Today, the demand for accelerated computing is skyrocketing, particularly in areas like AI and ML.
NVIDIA GPUs is the top choice in high-performance and accelerated computing. Their architecture is specifically designed to handle the parallel nature of computational tasks, making them indispensable for ML and AI workloads.
While NVIDIA GPUs deliver impressive performance, they are also expensive, and one of the primary challenges companies face is the efficient utilization of computational resources, particularly when it comes to GPU acceleration.
Efficient sharing and allocation of GPU resources can lead to significant cost savings, especially on containers.
We partnered with AWS for a live webinar where we will share deep and valuable insights on how to optimize GPU resources on EKS.
The content is very technical and includes a full demo.
CTOs, DevOps or Infrastructure Team Leads looking to optimize their AI/ML application's GPU costs.
AI or ML engineers thinking of taking their infrastructure to the next level.
In this webinar, our seasoned experts will provide a high level overview of leveraging Nvidia GPUs on AWS, share valuable insights and go-to guide of running and optimizing GPU resources on EKS clusters.
Doesn't matter where you are on your AI/ML Infrastructure journey, this session will equip you with the knowledge and tools to understand how everything works together.
Introduction to Nvidia on AWS
AWS Solutions for GPU Processing
Optimizing GPU Workloads on Kubernetes
Nvidia GPU Slicing on EKS - Use Cases & How-to
HPA with GPU Metrics
Proven tips and recommendations|
Live Q&A session
