Learn to build, containerize, deploy, and extend interactive 3D digital twin applications using NVIDIA Omniverse Kit App Streaming. This hands-on course walks you through creating a streaming-ready Kit app, deploying it for web clients, and enabling real-time, bi-directional data interaction—empowering scalable, remote visualization and collaboration across digital twin workflows.

Please RSVP and arrive at least 5 minutes before the start time, at which point remaining spaces are open to standby attendees.

Open models are essential for accelerating AI innovation, providing transparency, flexibility, and adaptability across enterprise use cases. They empower organizations to fine-tune, customize, and deploy AI solutions securely while retaining control over data. Discover how NVIDIA AI Foundation models, available on Azure platforms as NVIDIA NIM, power technologies and use cases. Learn how NVIDIA Nemotron, open reasoning models, are used to build powerful enterprise research agents that synthesize hours of research in minutes. Also, explore how NVIDIA Cosmos world foundation models are used to simulate, reason, and generate data for downstream pipelines in robotics, autonomous vehicles, and industrial vision systems.

Join Jen Hoskins, NVIDIA’s Global Head of Cloud & Partnerships for Startups, for a live showcase featuring cutting-edge developer tools from NVIDIA Inception and Microsoft for Startups. Learn from from the teams behind Qodo, CodeRabbit, Factory AI, Arize, and Galileo as they bring live demonstrations to life, and about how their technologies help developers build, debug, and monitor AI applications using accelerated computing and generative AI.

The Foundry Model Catalog supports “One-click” deployment of the latest NVIDIA NIM™ Examples of usage of the newest NVIDIA entries • Nemotron Nano: Large Language Reasoning Model • Nemotron Nano VLM: VLM with the ability to query and summarize images and video • Cosmos-reason1: Reasoning VLM for physical AI and robotics • Microsoft-Trellis: Asset generation model capable of producing detailed meshes, • Boltz2: Structural biology model for structure and affinity • Evo2 NIM: Biological model that integrates information over long genomic sequences

Please RSVP and arrive at least 5 minutes before the start time, at which point remaining spaces are open to standby attendees.

Découvrez comment poolside et NVIDIA collaborent pour révolutionner la productivité des développeurs dans les secteurs hautement réglementés, grâce à des modèles d'IA spécialement conçus pour le développement logiciel. De l'entraînement du modèle exploitant un large cluster de GPUs, à l'agent, en passant par votre IDE et votre CLI, vous comprendrez comment accélérer vos cycles de développement tout en respectant vos exigences les plus strictes en matière de conformité, de confidentialité et de souveraineté des données.

JAX is a key framework for LLM development, offering composable function transformations and a powerful bridge between low-level compilers and high-level code. To help address the challenges of moving from development to large-scale production, this talk introduces JAX-Toolbox, an open-source project that provides a robust foundation for the LLM development lifecycle. The session covers the CI/CD architecture that provides a stable foundation for JAX-based frameworks, how to build GPU-optimized containers for LLM frameworks such as MaxText and AXLearn to ensure reproducible workflows, and practical methods for deploying frameworks' containers on Kubernetes and SLURM-based clusters.

Anu Srivastava, Sr. Staff Technical Marketing Manager, NVIDIA - Anu leads open model marketing at NVIDIA, working with partners like Meta, Google DeepMind, and Microsoft to grow the public AI ecosystem.

This session offers a technical deep dive of the state-of-art AlloyDB AI capabilities for building highly accurate and relevant generative AI applications using real-time data. We’ll cover vector search using Google Research’s ScaNN index technology and cover how you can utilize Gemini from AlloyDB operators to seamlessly integrate into your application. Discover AlloyDB AI natural language feature, a new way to interact with databases and how it accurately and securely answers your questions. Also learn about the latest research between Google and NVIDIA on GPU-accelerated vector index builds in databases.

Deploy and scale containerized AI models with NVIDIA NIMs on Google Kubernetes Engine (GKE). In this interactive session, you’ll gain hands-on experience deploying pre-built NIMs, managing deployments with kubectl, and autoscaling inference workloads. Ideal for startup developers, technical founders, and tech leads.

**Please bring your laptop to get the most out of this hands-on session**

Is your platform ready for the scale of rapidly evolving models and agents? In this session, we’ll explore strategies for scaling your cloud native AI platform - empowering teams to leverage an increasing variety of AI models and agent frameworks. We’ll dive into tools and practices for maintaining control and cost efficiency while enabling AI engineering teams to quickly iterate on Google Kubernetes Engine (GKE). We’ll explore how NVIDIA NIM microservices deliver optimized inference with minimal tuning. 

This Session is hosted by a Google Cloud Next Sponsor.
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Build, deploy, and monetize AI agents on Google Cloud. This session covers the full lifecycle — development on Vertex AI, packaging, publishing, and commercialization on Google Cloud Marketplace, deployment on GKE etc., and customer access, including via Google Agentspace. We'll demonstrate leveraging the Google Cloud partner ecosystem to build enterprise-grade agents.

AI reasoning models and agents are reshaping the AI landscape, ushering in a new era of autonomous decision-making and action. Accelerated computing is crucial for delivering exceptional user experiences, enabling real-time responses and timely actions while reducing deployment costs at scale. The NVIDIA and Google Cloud partnership delivers cutting-edge AI infrastructure, enterprise-grade software, and optimized AI models, providing the foundation to build and deploy this new class of AI systems. Discover how NVIDIA AI, integrated across Google Cloud, offers developers flexibility and choice while delivering best-in-class performance and TCO, transforming enterprise AI in this new era.

This Session is hosted by a Google Cloud Next Sponsor.
Visit your registration profile at g.co/cloudnext to opt out of sharing your contact information with the sponsor hosting this session.

NVIDIA GPUs accelerate batch ETL workloads at significant cost savings and performance. In this session, we will delve into optimizing Apache Spark on GCP Dataproc using the G2 accelerator-optimized series with L4 GPUs via RAPIDS Accelerator For Apache Spark, showcasing up to 14x speedups and 80% cost reductions for Spark applications. We will demonstrate this acceleration through a reference AI architecture on financial transaction fraud detection, and go through performance measurements.

Unstructured data makes up the majority of all new data; a trend that's been growing exponentially since 2018. At these volumes, vector embeddings require indexes to be trained so that nearest neighbors can be efficiently approximated, avoiding the need for exhaustive lookups. However, training these indexes puts intense demand on vector databases to maintain a high ingest throughput. In this session, we will explain how the NVIDIA cuVS library is turbo charging vector database ingest with GPUs, providing speedups from 5-20x and improving data readiness.

This Session is hosted by a Google Cloud Next Sponsor.
Visit your registration profile at g.co/cloudnext to opt out of sharing your contact information with the sponsor hosting this session.

With great power comes great responsibility – and the need for confidentiality. This talk explores how confidential computing technology can act as a powerful shield for your AI deployments. We’ll show how Confidential Computing and confidential accelerators safeguard sensitive data and algorithms, even during processing, to ensure that your AI workloads remain protected throughout their life cycle.

This Session is hosted by a Google Cloud Next Sponsor.
Visit your registration profile at g.co/cloudnext to opt out of sharing your contact information with the sponsor hosting this session.

In this session learn about performance optimizations for PyTorch on Google Cloud accelerators using OpenXLA. These models are powerful but can be disrupted by resource failures. This talk also explores strategies for achieving greater resiliency when running PyTorch on GPUs, focusing on fault tolerance, checkpointing, and distributed training. Learn how to leverage open source tools to minimize downtime and ensure your deep learning workloads run smoothly.

Following our Kaggle Grandmaster Fireside Chat, selected participants will have the opportunity to talk more directly with the grandmasters in a small group. To select participants, we'll be inviting the folks who have submitted the best and most interesting questions). Sharpen your thinking cap and let us know what's really on your mind!

In this session, Andrew Sun, Director of Business Development for Retail Software and Cloud at NVIDIA will discuss the impact of GenAI on the retail market and its specific benefits its bringing to the clothing/sporting goods companies like Puma. In this session, we will also showcase some of the unique GenAI experiences Puma has built while leveraging GPUs on Google Cloud’s platform.

This Session is hosted by a Google Cloud Next Sponsor.
Visit your registration profile at g.co/cloudnext to opt out of sharing your contact information with the sponsor hosting this session.

In today's rapidly evolving technological landscape, enterprises face significant hurdles in developing and implementing mission-critical AI applications such as agentic or physical AI. This session will explore how organizations can harness the power of NVIDIA DGX Cloud on Google Cloud, a high-performance, fully-managed AI platform, to accelerate the next era of AI. Learn how to drive innovation in an increasingly competitive landscape, and achieve lower TCO.

This Session is hosted by a Google Cloud Next Sponsor.
Visit your registration profile at g.co/cloudnext to opt out of sharing your contact information with the sponsor hosting this session.

Hear from Kaggle Grandmasters from NVIDIA and beyond as they talk through how they approach competitions, current events in AI and answer your questions! We're inviting the folks who submit the best and most interesting questions to chat with the Grandmasters after in a small group setting. Sharpen your thinking cap and let us know what's really on your mind!

David Austin is a data scientist and a member of one of the top teams in Kaggle’s competition to detect AI-generated text. He’ll be sharing his first-hand learnings and insights on this rapidly growing problem space.

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JAX is an ML framework that has been widely adopted by foundation model builders because of its advantages like high performance, scalability, composability, and ease of programmability. In this session, we will showcase the entire ecosystem supported by JAX for end-to-end foundation model building from data loading to training and inference on both TPUs and GPUs. We'll highlight the entire JAX stack, including high performance implementations for large language models and diffusion models in MaxText and MaxDiffusion. Learn how customers such as Assembly AI, Cohere, Anthropic, MidJourney, Stability AI, and partners like Nvidia, have adopted JAX for building foundation models on Google Cloud and beyond.

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Any new technology brings potential new risks, and generative AI is no exception. In this session, we will cut through the FUD around gen AI deployments - helping you understand the real risks, both existing and novel, with deploying AI systems in the cloud. We’ll also provide a framework for how to support the safe use of AI in your organization, so that security, privacy, and risk concerns, real and imagined, can be addressed effectively without slowing business velocity.

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In this talk, we delve into the complexities of building enterprise AI applications, including customization, evaluation, and inference of large language models (LLMs). We start by outlining the solution design space and presenting a comprehensive LLM evaluation methodology. Then, we review state-of-the-art LLM customization techniques, introduce NVIDIA Inference Microservice (NIM) and a suite of cloud-native NVIDIA NeMo microservices for ease of LLM deployment and operation on Google Kubernetes Engine (GKE). We conclude with a live demo, followed by practical recommendations for enterprises.

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The future of cloud computing is shifting to private, encrypted services where people can be confident that their workloads stay verifiably isolated and protected. Explore the latest advances in confidential computing and confidential accelerators, and how they can be used to not only preserve your data confidentiality, but to free it for secure collaboration across teams, companies, and borders. You’ll see demos focusing on providing holistic data confidentiality and AI/ML model protection. Welcome to the future of secure data freedom!

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Generative AI is driving the next industrial revolution and businesses are racing to use it to improve customer experiences, healthcare, and operations. But to succeed, they need a plan. In this session, we'll highlight practical use cases and challenges of scaling AI in 2024. We'll also show you how NVIDIA DGX Cloud on Google Cloud supports the entire AI app lifecycle, from development to deployment. You'll learn how to speed up the return on investment of AI.

By attending this session, your contact information may be shared with the sponsor for relevant follow up for this event only.

Click the blue “Learn more” button above to tap into special offers designed to help you implement what you are learning at Google Cloud Next 25.