AI’s rapid evolution demands purpose-built computing infrastructure that balances cost-efficiency, scalability, and high performance. Training deep learning models requires massive computational power, pushing traditional hardware’s limits. Cloud providers and enterprises are seeking cost-effective yet robust solutions to train increasingly complex models while optimizing energy consumption and reducing latency. As enterprises increasingly adopt AI-driven solutions, cloud providers are taking the lead in innovation to meet these growing demands. Cloud providers such as AWS have been at the forefront of this transformation, investing heavily in custom AI chips to enhance deep learning training and inference capabilities.
AWS’ Trainium and Inferentia stand out as powerful, cost-effective alternatives to conventional hardware. These chips enable organizations to scale AI applications seamlessly, reduce dependency on expensive third-party hardware, and enhance performance across cloud-based machine learning workflows.
In this report, we explore the potential of Trainium2, an AWS chip launched at re:Invent 2024, examining its key features, including cost efficiency, ease of integration into different environments, and security and privacy concerns. We also provide insights into its market positioning, key benefits, and broader implications for enterprises.
Scope
All industries and geographies
Contents
In this report, we:
- Provide an overview of the Trainium2 chip that AWS launched at their re:Invent 2024 event
- Examine its current positioning and alignment in the AI infrastructure market
- Explore its specifications, benefits, and challenges
Membership(s)
Cloud and Infrastructure Services
Artificial Intelligence (AI)
Sourcing and Vendor Management
