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BUILD APPLICATIONS WITH GENERATIVE AI

Experience, prototype, and deploy AI with production-ready APIs that run
anywhere.

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TUTORIALS

April 03, 2024


OPTIMIZING MEMORY AND RETRIEVAL FOR GRAPH NEURAL NETWORKS WITH WHOLEGRAPH, PART
2


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April 03, 2024


NEW LAB: GENERATIVE AI INFERENCE WITH NVIDIA NIM


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April 02, 2024


TUNE AND DEPLOY LORA LLMS WITH NVIDIA TENSORRT-LLM


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LATEST RELEASES


 * CUDA Toolkit 12.4
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NEWS

March 19, 2024


GENERATIVE AI FOR DIGITAL HUMANS AND NEW AI-POWERED NVIDIA RTX LIGHTING


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March 19, 2024


NVIDIA SPEECH AND TRANSLATION AI MODELS SET RECORDS FOR SPEED AND ACCURACY


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March 19, 2024


BOOST MULTI-OMICS ANALYSIS WITH GPU-ACCELERATION AND GENERATIVE AI


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March 19, 2024


BREAKING BARRIERS IN HEALTHCARE WITH NEW MODELS FOR GENERATIVE AI AND CELLULAR
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CHECK OUT RECOMMENDED GTC 2024 SESSIONS

TRANSFORMING AI

The Transforming AI Panel features the authors of "Attention Is All You Need,"
the groundbreaking paper that introduced the transformer neural network
architecture. Transformers have since dominated all areas of AI and
revolutionized the industry. Join Ashish Vaswani, Noam Shazeer, Niki Parmar,
Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia
Polosukhin, hosted by NVIDIA founder and CEO Jensen Huang.

See Session

NAVIGATING THE LARGE LANGUAGE MODELS FRONTIER: PRACTICAL STRATEGIES FOR BUILDING
ENTERPRISE APPLICATIONS POWERED BY LLMS

Our panel of experts will talk about the best practices for building robust
large language model (LLM)-based enterprise applications that deliver value and
efficiency. Products such as ChatGPT have demonstrated the unprecedented power
of LLMs in processing information and generating content. But harnessing LLMs
for building enterprise applications introduces a spectrum of intricate
challenges. They include, but aren't limited to, managing the behavior of LLMs
(e.g., avoiding hallucination), adapting LLMs to domain-specific tasks while
pre-trained on very general domain corpora, interacting with agents to execute
some specific tasks, latency, security, and so on. We'll explore how enterprises
can address these challenges and exploit the full potential of LLMs for their
applications.

See Session

CUDA: NEW FEATURES AND BEYOND

The CUDA platform is the foundation of the GPU computing ecosystem. Every
application and framework that uses the GPU does so through CUDA's libraries,
compilers, runtimes and language — which means CUDA is growing as fast as its
ecosystem is evolving. At this engineering-focused talk, you'll learn from one
of the architects of CUDA about all that's new and what's coming next, for both
CUDA and GPU computing as a whole.

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EARLY SCIENCE WITH GRACE HOPPER AT SCALE ON ALPS

We'll introduce early science results and HPC performance of the ETH EXCLAIM
project running the ICON climate model on the Alps system infrastructure. We'll
focus especially on system and software configuration requirements that optimize
the balance of performance between Grace and Hopper under given power capping
constraints. Initial performance investigations will begin with climate science,
with the intent to inform and inspire other domains and applications. The target
will be performance considerations for the ICON coupling of atmosphere and
ocean, but early science results may be limited to an aqua planet model
configuration.

See Session

DEPLOYING, OPTIMIZING, AND BENCHMARKING LARGE LANGUAGE MODELS WITH TRITON
INFERENCE SERVER

Learn how to serve large language models (LLMs) efficiently using Triton
Inference Server with step-by-step instructions. NVIDIA Triton Inference Server
is an open-source inference serving solution that simplifies the production
deployment of AI models at scale. With a uniform interface and standard set of
metrics, developers can easily deploy deep learning and machine learning models
across many different frameworks (TensorRT, TensorRT-LLM, vLLM, TensorFlow,
PyTorch, OpenVINO, and more) on multiple types of hardware (CPU and GPU). We’ll
review the challenges of serving LLMs and demonstrate how Triton Inference
Server’s latest features help overcome them. We’ll cover how to easily deploy an
LLM across multiple backends and compare their performance, as well as how to
fine-tune deployment configurations for optimal performance. We'll provide
step-by-step instructions for anyone to follow using publicly available
collateral and answer questions along the way.

See Session

A DEEP DIVE INTO THE LATEST HPC SOFTWARE

Take a deep dive into the latest developments in NVIDIA software for high
performance computing applications, including a comprehensive look at what’s new
in programming models, compilers, libraries, and tools. We'll cover topics of
interest to HPC developers, targeting traditional HPC modeling and simulation,
quantum computing, HPC+AI, scientific visualization, and high-performance data
analytics.

See Session

ROBOTICS AND THE ROLE OF AI: PAST, PRESENT, AND FUTURE

Advances in artificial intelligence have enabled breakthroughs in several
fields, including computer vision and natural language processing in both
academia and industry. In this fireside conversation, NVIDIA’s senior director
of robotics research, Dieter Fox, will be joined by Marc Raibert, the executive
director of The AI Institute, to discuss how artificial intelligence has
impacted robotics, from the traditional controls era to today.

See Session

DIGITALIZING THE WORLD'S LARGEST INDUSTRIES WITH OPENUSD AND GENERATIVE AI

The world’s largest industries are racing to become software-defined, but
digitalization of such processes is complex. Hear from this panel of
distinguished luminaries on their industrial digitalization projects that infuse
generative AI, new data platforms, 3D interoperability, and advanced
visualization throughout their organizations. Want to learn more about OpenUSD
for industrial digital twins? Attend this deep learning institute training
course.

See Session

ROBOTICS IN THE AGE OF GENERATIVE AI

Generative AI is taking automated common-sense reasoning, task planning, and
perception to a new level. It is also revolutionizing synthetic data generation,
human-computer interaction, and multimodal understanding. Collectively, these
are some of the key capabilities required for robots to understand our world and
provide humanity with accessible, versatile physical assistance for day-to-day
tasks. The key missing ingredient is for generative AI to also understand
physical interaction. I'll sketch a future in which embodied AI is a natural
extension of the revolution that large multimodal models are ushering, and its
implications for the future of collaborative robotics and human-centered AI at
large.

See Session

INSIGHTS FROM NVIDIA RESEARCH

We'll share some insights from NVIDIA Research for the past year. These will
include a power-efficient “always-on” AI accelerator, a diffusion model that
improves the resolution of weather predictions, a large language model-powered
embodied agent, and a foundation model for autonomous vehicle scene
reconstruction.

See Session

ACCELERATING AUTOMOTIVE WORKFLOWS WITH LARGE LANGUAGE MODELS

Large language models (LLM) are revolutionizing the way we interact with
information, making it easy to pinpoint information from a large source of data,
such as vehicle owner’s manuals or manufacturing machinery manuals. However,
ensuring these agents operate accurately, or without hallucinating, presents a
variety of challenges. To date,the best solutions use LLMs through the retrieval
augmented generation (RAG) architecture, with solutions benefiting from
fine-tuning LLMs for performance, scalability, and domain knowledge. This
session will demonstrate and discuss such LLM solutions for vehicle engineering,
connected vehicle analytics, manufacturing, legal, vehicle service and repair,
customer support, and employee support.

See Session

INTELLECTUAL PROPERTY CHALLENGES IN THE AGE OF GENERATIVE AI

Generative AI is challenging traditional concepts of intellectual property
rights for content and emerging technology, including copyright, trade secrets,
and patents. This fireside chat will offer U.S. perspectives on intellectual
property in light of new technological advances. This content is produced by the
USPTO.

See Session

DRIVING ENTERPRISE TRANSFORMATION: CIO INSIGHTS ON HARNESSING GENERATIVE AI'S
POTENTIAL

Generative AI is leading a transformative era for enterprises, with vast
potential to enhance employee experiences, improve productivity, strengthen
security, and drive operational efficiencies. Our esteemed panel of chief
information officers will explore how they harness Generative AI in their
organizations. AI's promises are accompanied by organizational and technical
challenges. CIOs grapple with structuring AI and transformation programs,
acquiring essential skills, and establishing guardrails for data governance,
security, hallucinations, and toxicity in Generative AI deployments. They also
consider build-versus-buy options and analyze cost-benefit and
total-cost-of-ownership dynamics of Generative AI solutions. Moderated by
NVIDIA's vice president of enterprise AI and automation, this session provides
practical insights and best practices from CIOs at the forefront of enterprise
transformation through Generative AI. Join us to explore the future of AI in the
enterprise.

See Session

REGULATING AI: GLOBAL PERSPECTIVES

Governments around the world are grappling with how to regulate the development,
deployment, and use of AI. This panel explores different policy and regulatory
approaches being considered and how companies should interpret this rapidly
shifting regulatory landscape.

See Session

LESSONS ON VIDEO GENERATION MODELS FROM RESEARCH TO PRODUCTION

We'll describe the journey of bringing Runway's Gen-1 and Gen-2 video generation
models to production, starting from the research efforts to develop and train
those models and going all the way to deploying them to Runway's suite of
creative tools, used by millions.

See Session

MACHINE LEARNING HAS TAKEN WEATHER FORECASTING BY STORM. HOW ABOUT CLIMATE
MODELING?

For 50 years, weather forecast and climate models have been written in Fortran
using numerical analysis, physical knowledge, and expert judgment. They have
slowly become more skillful with the help of better observations and finer
grids. In the past two years, data-driven ML has surpassed the skill of the best
forecast models. What can the new kid in town learn to do next? Climate change
is a defining issue of the 21st century. Can ML help us model climate, even
though the future will not be like the past? Can it help us plan for coming new
extremes of heat, flood, drought, and rising sea levels? Indeed, ML may soon
become a backbone of climate modeling, saving time and money and making
reliable, customized, local climate information much more broadly accessible.
You’ll see promising early steps toward that vision.

See Session



ON-DEMAND VIDEOS

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The Fast Path to Developing with LLMs
Bringing Zero-Code Change Acceleration to …
Unlocking AI Model Performance: Exploring …
Tailoring LLMs to Your Use Case
Part 1 Overview and installation of Video …
Fullbody ARKit Workflow with Audio2Face and …
Exploring Efficient Tools for Autonomous Vehicle …
Vector Search: Exploring Applications, Techniques,…
Power Networks for AI Clouds
Reinventing the Complete Cybersecurity Stack …
Taming LLMs with the Latest Customization …