Deploy Faster Generative AI models with NVIDIA NIM on GKE

Before diving into the hands-on lab, learn more about AI inference and how to leverage joint NVIDIA and Google Cloud solutions to improve performance, keep data secure, and perform low-latency and high-throughput inference tasks.

This hands-on Codelab will guide you through deploying an AI model on Google Kubernetes Engine (GKE) using the power of NVIDIA NIM™ microservices.

This tutorial is designed for developers and data scientists who are looking to:

• Simplify AI inference deployment: Learn how to use NVIDIA NIM for faster and easier deployment of AI models into production on GKE.
• Optimize performance on NVIDIA GPUs: Gain hands-on experience with deploying containerized AI models that use NVIDIA TensorRT for optimized inference on GPUs within your GKE cluster.
• Scale AI inference workloads: Explore how to use Kubernetes for autoscaling and managing compute resources for your deployed NIMs based on demand.

Want to learn more about GPUs in Google Kubernetes Engine (GKE)? Read this article to learn how you can use GPUs to accelerate resource-intensive tasks, like machine learning and data processing.