#637: Plan AI development cycles with Amazon EC2 Capacity Blocks for ML
Nov 13, 2023
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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.
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.
Use Cases for EC2 Capacity Blocks
EC2 capacity blocks are particularly useful for customers in different stages of their machine learning development cycle. It is ideal for initial experimentation and prototype development as customers can start with a smaller size capacity block and then reserve a larger block for longer periods as they progress in training larger AI models. Capacity blocks are also suitable for fine-tuning models with new data or handling expected surges in demand for deployed models, such as during product launches. However, capacity blocks are not intended to replace on-demand capacity reservations, which are better for covering long-term steady-state usage needs.
Benefits and Pricing of EC2 Capacity Blocks
EC2 capacity blocks offer several benefits for customers. They provide assurance of GPU instance capacity at a specific future date, allowing for better planning of machine learning development cycles. The blocks ensure predictable access to P5 instances powered by NVIDIA H100 GPUs, without requiring long-term commitments. Additionally, capacity blocks are delivered within EC2 ultra clusters, ensuring low latency and high throughput network connectivity. Pricing for capacity blocks is dynamic, based on supply and demand. Customers pay upfront for the blocks, but during the reservation time, they only incur additional costs if using a premium operating system. To get the lowest price, customers can search across a wide date range. Currently, capacity blocks are available in the US East Ohio region.
Tune in to listen to Jillian Forde and Jake Siddall (AWS Senior Product Manager) dive deep into a new service called Amazon EC2 Capacity Blocks for ML. EC2 Capacity Blocks enable you to reserve GPU instances in Amazon EC2 UltraClusters to run ML workloads.
Amazon EC2 Capacity Blocks for ML product details: https://bit.ly/3StmbR8
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