AWS Podcast
#637: Plan AI development cycles with Amazon EC2 Capacity Blocks for ML
Nov 13, 2023
Jillian Forde and Jake Siddall discuss Amazon EC2 Capacity Blocks for ML, a new service that allows customers to reserve GPU instances for machine learning workloads. They explore the benefits of capacity blocks, use cases, planning AI strategy, pricing dynamics, and optimizing compute capacity.
18:11
Episode guests
AI Summary
AI Chapters
Episode notes
Podcast summary created with Snipd AI
Quick takeaways
- EC2 Capacity Blocks for ML enable customers to reserve GPU instances in Amazon EC2 UltraClusters for machine learning workloads.
- Capacity blocks are beneficial for different stages of machine learning development cycles, providing flexibility and cost efficiency for customers with fluctuating GPU capacity needs.
Deep dives
Introduction to EC2 Capacity Blocks for Machine Learning
EC2 capacity blocks are a new usage model in Amazon EC2 designed to simplify and democratize machine learning. Customers can reserve GPU instances for the exact duration they need to train and deploy machine learning and generative AI models. By reserving capacity only when needed, customers can avoid holding onto underutilized GPU instances. Capacity blocks are currently available for key five instances powered by NVIDIA H100 tensor core GPUs. They can be easily reserved through the EC2 console. This new reservation model provides flexibility and cost efficiency for customers with fluctuating GPU capacity needs.
Remember Everything You Learn from Podcasts
Save insights instantly, chat with episodes, and build lasting knowledge - all powered by AI.
