Join our list for notifications and early access to events
Today, we're joined by Ron Diamant, chief architect for Trainium at Amazon Web Services, to discuss hardware acceleration for generative AI and the design and role of the recently released Trainium2 chip. We explore the architectural differences between Trainium and GPUs, highlighting its systolic array-based compute design, and how it balances performance across key dimensions like compute, memory bandwidth, memory capacity, and network bandwidth. We also discuss the Trainium tooling ecosystem including the Neuron SDK, Neuron Compiler, and Neuron Kernel Interface (NKI). We also dig into the various ways Trainum2 is offered, including Trn2 instances, UltraServers, and UltraClusters, and access through managed services like AWS Bedrock. Finally, we cover sparsity optimizations, customer adoption, performance benchmarks, support for Mixture of Experts (MoE) models, and what’s next for Trainium.
I’d like to send a big thanks to our friends at AWS for their support of the podcast and their sponsorship of today’s episode. In this interview, I speak with chief architect Ron Diamant about the silicon, server, and software innovations in AWS Trainium2, Amazon's latest purpose-built AI chip. AWS Trainium and Inferentia are pushing the price-performance frontier in AI infrastructure, delivering up to 30-50% better price performance for training and inference. These chips power AI workloads for genAI pioneers like Anthropic, mature enterprises like Ricoh, and innovative startups like NinjaTech. So, if you are ready to optimize your AI infrastructure costs while maintaining high performance, you should definitely explore AWS AI chips. Visit twimlai.com/go/trainium to learn more.
