Accelerating AI Inference Workloads with Google Cloud TPUs and GPUs

Explore key considerations for accelerating AI inference workloads in this 14-minute video featuring a discussion between Debi Cabrera and Alex Spiridonov, Group Product Manager at Google Cloud. Learn about balancing cost and efficiency when choosing between cloud tensor processing units (TPUs) and NVIDIA-powered graphics processing unit (GPU) VMs for deploying AI models at scale. Discover the differences between TPUs and GPUs for various AI models, and gain insights on getting started with Google Cloud's offerings. Address common challenges in inference optimization and explore available resources for AI inference workloads. The video covers topics such as cost implications, deployment strategies, and optimization techniques for inference pipelines on Google Cloud.

- Meet Alex
- Balancing cost and efficiency
- TPU vs GPU for AI models
- Getting started with Google Cloud TPUs and GPUs
- Common challenges when using inference optimization
- Available resources for AI inference workloads
- Wrap up