Title

AWS re:Invent 2022 - Introducing AWS Inferentia2-based EC2 Inf2 instances (CMP334)

Summary

• AWS announced the launch of Amazon EC2 Inf2 instances featuring the new ML accelerator, Inferentia2.
• Joe Sinurchia, a product manager on the Amazon EC2 team, presented the session with customer experiences from Qualtrics and Amazon CodeWhisperer.
• The session covered AI and ML innovations, democratizing inference with AWS Inferentia, and high-performance natural language processing inference.
• Inf2 instances offer 4x the throughput and 10x lower latency compared to Inf1 instances, with 10 TB/s aggregated memory bandwidth across 384 GB of total accelerator memory.
• Inf2 instances support dynamic input and control flow operators, come in four sizes, and can deploy models up to 175 billion parameters.
• Performance metrics for inference include latency and throughput, with Inf2 allowing high throughput and low latency without trade-offs.
• Inf2 instances are energy efficient, providing up to 50% better performance per watt compared to GPU instances optimized for inference.
• Customers like Qualtrics have seen cost savings and performance improvements by using AWS Inferentia.
• Inferentia2 introduces innovations such as dynamic execution, support for six unique data types, and distributed inference with high-speed connectivity between accelerators.
• AWS Neuron SDK makes it easy to deploy models on Inferentia2, integrating with machine learning frameworks like PyTorch and TensorFlow.
• Amazon CodeWhisperer shared their experience with large language models and how Inf2 will help address their challenges in cost optimization, latency, and ease of deployment.

Insights

• The launch of Inf2 instances represents a significant advancement in AWS's commitment to providing high-performance, cost-effective machine learning inference solutions.
• The Inferentia product line, including Inf1 and Inf2, is being adopted by a wide range of customers, from startups to large enterprises, indicating a strong market demand for specialized ML inference hardware.
• The emphasis on energy efficiency and reduced environmental impact aligns with growing concerns about the sustainability of cloud computing services.
• The support for dynamic execution and multiple data types in Inferentia2 shows AWS's focus on flexibility and future-proofing their ML offerings.
• The introduction of distributed inference capabilities in Inf2 instances addresses the challenges of deploying very large models that cannot fit into a single chip, which is becoming increasingly common as model sizes grow.
• AWS Neuron SDK's ease of use and integration with popular ML frameworks can lower the barrier to entry for customers looking to leverage AWS's ML hardware, potentially accelerating adoption.
• The experiences shared by Qualtrics and Amazon CodeWhisperer highlight real-world applications and benefits of using AWS Inferentia, providing valuable case studies for potential customers considering the platform.