Jianying Lang

Physical AI is transforming robotics and autonomous systems with world models that enable agents to understand and interact with their environments. Training and deploying these large-scale vision-language-action (VLA) models requires complex distributed infrastructure.

AWS Trainium is purpose-built to deliver superior price-performance for LLM training workloads, making it cost-efficient for model development tasks including training, fine-tuning, and inference. Its SDK, Neuron, seamlessly integrates with open-source libraries like Hugging Face Optimum-Neuron for ease of use.

Large language models (LLMs) show remarkable impact in a wide range of applications, but they pose significant cost and deployment challenges at scale. In this workshop, explore best practices for efficient LLM deployment using AWS Inferentia2-based Amazon EC2 Inf2 instances to maximize performance and lower costs.

Large language models (LLMs) are applied for use cases such as chatbots, enterprise search, and coding companions. AWS Inferentia2 is ideal for LLM inference, providing high performance at up to 40% lower cost per inference.

Large language models (LLMs) are pretrained on vast amounts of data and perform well across a variety of general-purpose tasks and benchmarks without further specialized training.