NVIDIA and Google Cloud Join Forces to Supercharge AI Development

At the annual Google I/O conference, NVIDIA and Google Cloud announced enhancements to their joint developer community, which now supports over 100,000 AI builders. This initiative provides curated learning paths, hands-on labs, and events designed to help developers leverage the full NVIDIA AI stack on Google Cloud infrastructure.

A Thriving Hub for AI Innovators

Launched during last year’s Google I/O, the community has rapidly become a central resource for data scientists, ML engineers, and developers aiming to refine their AI skills using the latest technologies from both companies. Over the past year, members have built production-ready retrieval-augmented generation (RAG) applications on Google Kubernetes Engine (GKE) and implemented observability for agent workloads. They have also explored cutting-edge LLM research and prototyped hybrid on-premises and cloud inference for real-world scenarios such as sports analytics and enterprise data pipelines.

Fresh Learning Paths and Hands-On Experiences

This year’s updates include a dedicated learning path for using the JAX library on NVIDIA GPUs, a NVIDIA Dynamo codelab focused on inference optimizations, and monthly developer livestreams. These resources enable developers to dive deeper into performance-critical areas and stay current with evolving AI workflows.

Mastering JAX on NVIDIA GPUs

NVIDIA and Google Cloud have collaborated closely on JAX, an open-source framework for high-performance numerical computing. The new learning path covers everything from single-GPU experiments to multi-rack deployments, ensuring a consistent and highly performant experience. This work extends to Google Cloud AI Hypercomputer, where the MaxText framework leverages JAX optimizations to train large models efficiently on NVIDIA GPUs.

Optimizing Inference with NVIDIA Dynamo on GKE

Another key addition is the NVIDIA Dynamo on GKE inference codelab. Dynamo helps developers optimize large-scale inference, including mixture-of-experts (MoE) models, making it easier to serve AI applications efficiently on NVIDIA accelerated infrastructure within Google Cloud.

Building with Google DeepMind’s Gemma, NVIDIA Nemotron, and Open Frameworks

The collaboration provides developers with a rich ecosystem of resources that combine NVIDIA libraries, open models, and tools with Google Cloud’s AI platform. For example:

• Accelerate data science using NVIDIA cuDF in Google Colab Enterprise or Dataproc.
• Deploy multi-agent applications by integrating Google DeepMind’s Gemma 4 models, NVIDIA Nemotron open models, and the Google Agent Development Kit with G4 VMs powered by NVIDIA RTX PRO 6000 Blackwell GPUs in Google Cloud Run or spot instances.

These capabilities empower developers to build, scale, and productize advanced AI solutions faster than ever before.

Coming Next Month

Members of the Google Cloud and NVIDIA developer community can look forward to the release of the new JAX learning path and the NVIDIA Dynamo on GKE codelab next month. These additions will provide hands-on experience with some of the most critical technologies shaping the future of AI development.

Empowering the Next Wave of AI Builders

By continuously expanding this joint developer community, NVIDIA and Google Cloud are ensuring that AI builders have the resources, tools, and infrastructure they need to turn innovative ideas into production-ready solutions. Whether experimenting with new models or optimizing inference at scale, developers can count on a robust ecosystem that accelerates every step of the AI journey.