Accelerate innovation with Google Cloud and NVIDIA

Join us at GTC 2026 to see how Google Cloud and NVIDIA are architecting the future of AI scale, security, and agentic workflows. Connect with our industry and product leaders during our exclusive sessions and happy hour to explore the breakthroughs shaping the next frontier of innovation.

Marquee and fireside chats

• Advancing to AI's Next Frontier: Insights From Jeff Dean and Bill Dally. Speakers: Jeff Dean (Chief Scientist, Google DeepMind) and Bill Dally (NVIDIA) March 18 | 4:00 PM–5:00 PM PT
• Enterprise AI Platforms: Scaling AI to Business Value. Speaker: Ed Chi (VP of Research, Google DeepMind) March 18 | 2:00 PM–2:40 PM PT

Infrastructure and scalability

• Blueprint for AI Scale: How Industry Architects Success. Panel Discussion hosted by Jason Monden, featuring customers WPP, General Motors, and Schrödinger, Inc. March 16 | 4:00 PM PT
• Scale Foundation Models with Google Cloud AI Hypercomputer. Speaker: Pramod Ramarao, March 17 | 10:00 AM PT
• NVIDIA GPU Acceleration on Snap’s Experimentation Platform. Cost-benefit analysis and million-dollar savings. March 17 | 1:00 PM PT

AI agents and applied research

• Puma’s Smart Stores: Agents and Digital Humans Redefine Interaction. Speakers: Kapil Dabi (Google) with Puma and Live X. March 18 | 10:00 AM PT
• Reinforcement Learning: Turning AI Agents Into Continuous Learners. Speakers: Robert Van Dussen (Google) and Atlassian. March 18 | 11:00 AM PT
• Introduction to Newton Physics Engine for Robotics. Speaker: Erik Frey (Simulation Lead, Google DeepMind). March 19 | 11:00 AM PT

Security and confidential computing

• Securing AI Applications: Red Teaming Models and Apps. Speaker: Heather Adkins (VP Security Engineering, Google). March 18 | 4:00 PM–4:40 PM PT
• From Isolation to Integration: Evolving Confidential Computing for a Scalable, Secure Future. Speaker: Nelly Porter (Director of Product Management, Google). March 19 | 10:00 AM PT

Hands-on labs and tutorials

• Training Lab: Inspecting Multimodal Data with Nemotron and Gemma 3. Instructor: Sandeep Sadasivuni (ISV Customer Engineer, Google). March 19 | 10:00 AM–11:45 AM PT

Networking and ecosystem

• Cheers to the Future of AI with Google Cloud Happy Hour. Connect with Google Cloud’s AI startup, Industry, and product leaders to discuss generative AI breakthroughs, infrastructure, and the future of the ecosystem. March 17, 2026 5:30 PM (RSVP Required)

Accelerate machine learning, scientific computing, and generative AI with high-performance GPUs on Google Cloud.

Key Benefits:

• Expedite workloads (generative AI, 3D visualization, HPC) with advanced AI hardware/software
• Access diverse GPUs for varied performance and pricing
• Optimize workloads with flexible pricing and machine customizations

Key Features

• Diverse GPU Offerings: Compute Engine offers NVIDIA GPUs: RTX PRO 6000, GB300, GB200, B200, H200, H100, L4, P100, P4, T4, V100, A100. Options cover various cost/performance needs.
• Adaptable Performance: Achieve optimal balance of processor, memory, high-performance disk, and up to 8 GPUs per instance. Benefit from per-second billing.
• Leverage Google Cloud Advantages: Run GPU workloads on Google Cloud and access industry-leading storage, networking, and data analytics.

Google Kubernetes Engine (GKE)

Leverage GKE's scalability, NVIDIA Multi-Instance GPU (MIG) support, and GPU time-sharing for efficient generative AI training, inference, and other compute-intensive workloads. Optimize resource utilization and minimize operational costs.

Vertex AI

Combine NVIDIA accelerated computing with Vertex AI, a unified MLOps platform. Utilize NVIDIA GPUs and AI software (such as, Triton™ Inference Server) within Vertex AI Training, Prediction, Pipelines, and Notebooks to accelerate generative AI development and deployment without infrastructure complexities.

Cloud Run

Deploy generative AI faster with NVIDIA NIM on Cloud Run, a fully managed serverless platform. Cloud Run's GPU support allows NIM to optimize performance and accelerate gen AI model deployment in a serverless environment.

Dynamic Workload Scheduler

Access NVIDIA GPU capacity on Google Cloud for short-duration AI workloads (training, fine-tuning, experimentation). Flexible scheduling and atomic provisioning enhance resource utilization and optimize costs across services like GKE, Vertex AI, and Batch.

Google Distributed Cloud

The NVIDIA Blackwell platform on Google Distributed Cloud enables secure, on-premises deployment of advanced agentic AI (including Google Gemini models). This offers breakthrough AI performance and scalability for sensitive, regulated workloads, ensuring data privacy, sovereignty, and compliance.

Google Cloud basics

• GPUs on Compute Engine: Compute Engine provides GPUs that you can add to your virtual machine instances. Learn more
• Using GPUs for training models in the cloud: Accelerate the training process for many deep learning models, like image classification, video analysis, and natural language processing. Learn more
• Attaching GPUs to Dataproc clusters: Attach GPUs to the master and worker Compute Engine nodes in a Dataproc cluster to accelerate specific workloads, such as machine learning and data processing. Learn more
• Using GPUs with Dataflow: Using GPUs in Dataflow jobs lets you accelerate some of your machine learning and other compute intensive data processing tasks. Learn more

Tutorials

• Learn how to add or remove GPUs from a Compute Engine VM. Learn more
• Installing GPUs Drivers: This guide shows ways to install NVIDIA proprietary drivers after you’ve created an instance with one or more GPUs. Learn more
• GPUs on Google Kubernetes Engine: Learn how to use GPU hardware accelerators in your Google Kubernetes Engine clusters’ nodes. Learn more

View all product documentation