Empowering AI Builders: NVIDIA and Google Cloud's Developer Community Q&A

Welcome to the forefront of AI innovation! At this year's Google I/O conference, NVIDIA and Google Cloud unveiled exciting updates to their joint developer community. This hub now serves over 100,000 developers, data scientists, and ML engineers looking to master AI with the full-stack NVIDIA platform on Google Cloud. Below, we answer key questions about the community's growth, new learning paths, and hands-on resources—so you can start building production-ready AI applications faster.

What is the NVIDIA and Google Cloud developer community, and why was it launched?

The community was launched at Google I/O last year as a collaborative space where developers, data scientists, and machine learning engineers can sharpen their AI skills using the latest NVIDIA and Google Cloud technologies. It provides curated learning paths, hands-on labs, and live events that guide members through building applications with the full-stack NVIDIA AI platform on Google Cloud. Over the past year, the community has become a go-to hub for AI builders—enabling them to experiment with large language models, prototype hybrid inference, and deploy production-ready solutions like retrieval-augmented generation (RAG) on Google Kubernetes Engine. With more than 100,000 members, the community now serves as a vital resource for accelerating AI development from concept to deployment.

Empowering AI Builders: NVIDIA and Google Cloud's Developer Community Q&A — Source: blogs.nvidia.com

What new additions have been made to the community this year?

This year brings several fresh resources for community members. A new learning path focuses on using the JAX library on NVIDIA GPUs, helping developers scale from single-GPU experiments to multi-rack deployments. There is also a new NVIDIA Dynamo codelab that dives into inference optimization—especially for mixture-of-experts models—allowing developers to serve AI more efficiently on GKE. Additionally, the community now hosts monthly developer livestreams where experts share tips, best practices, and real-world use cases. These additions complement existing resources and ensure that builders have access to the most current tools and techniques for AI development on Google Cloud.

How do the JAX and NVIDIA Dynamo resources help developers?

The JAX learning path equips developers with end-to-end guidance on running and scaling JAX workloads on NVIDIA GPUs. It covers everything from initial setup on a single GPU to orchestrating multi-rack clusters, all while maintaining strong performance and a consistent experience on Google Cloud's AI Hypercomputer. The MaxText framework, which uses JAX optimizations, is highlighted for efficient large model training. Meanwhile, the NVIDIA Dynamo on GKE codelab teaches inference optimization strategies—critical for deploying large language models with minimal latency. By combining these resources, developers can build and serve AI applications that are both fast and cost-effective, leveraging NVIDIA's accelerated infrastructure and Google Cloud's managed Kubernetes services.

What real-world applications have emerged from the community so far?

Community members have already produced impressive results. Some have built production-ready retrieval-augmented generation (RAG) applications on Google Kubernetes Engine, enabling accurate document Q&A by combining retrieval and generation. Others have instrumented observability for agent workloads, ensuring reliable AI-powered systems. Experimental work includes large language model research and prototyping hybrid inference setups that span on-premises and cloud environments. Specific use cases range from sports analytics—processing real-time game data—to enterprise data pipelines that accelerate ETL tasks with NVIDIA cuDF in Google Colab Enterprise or Dataproc. These examples demonstrate the community's role in turning cutting-edge AI into practical, business-ready solutions.

How does the collaboration with Google DeepMind and open frameworks like Gemma and Nemotron benefit developers?

NVIDIA and Google Cloud have partnered to provide learning resources and hands-on labs that combine NVIDIA libraries, open models, and Google Cloud's AI platform. Developers can use Google DeepMind's Gemma 4 models alongside NVIDIA Nemotron open models and the Google Agent Development Kit to build multi-agent applications. These tools run on Google Cloud G4 VMs powered by NVIDIA RTX PRO 6000 Blackwell GPUs, available in Cloud Run or with spot instances for cost savings. The integration with open frameworks like JAX ensures that developers can seamlessly move from experimentation to production. This collaboration reduces the complexity of assembling AI stacks, allowing engineers to focus on creating innovative applications with optimized performance from day one.

What steps should a new developer take to get involved with this community?

Getting started is easy. Visit the joint NVIDIA and Google Cloud developer community page to sign up for free. Once registered, you can explore curated learning paths—such as the new JAX on NVIDIA GPUs track—and access hands-on codelabs like NVIDIA Dynamo on GKE. Monthly livestreams and events provide opportunities to learn from experts and connect with peers. To accelerate your journey, begin with the learning path that matches your current skill level, then dive into the labs to apply what you've learned. The community also offers forums and discussion boards where you can ask questions and share your projects. Whether you're a data scientist, ML engineer, or hobbyist, these resources will help you build, optimize, and deploy AI applications faster on Google Cloud.