NVIDIA and Partners to Explore AI Advancements at GTC 2025 Conference

Generative AI: The Game Changer in Computing

Generative AI is shaking up the world of computing in ways we could only dream about a few years ago. Imagine a future where building, training, and optimizing AI models on your personal computer or workstation is not just possible but incredibly efficient. We’re talking about everything from content creation to software development. With AI-powered PCs and workstations, workflows are being transformed, and productivity is hitting new heights. If you’re curious about what this all means, you won’t want to miss GTC 2025, happening from March 17-21 at the SAN Jose Convention Center. It’s a chance to hear from experts across the AI ecosystem about how to deploy AI locally, optimize models, and leverage hardware and software to supercharge AI workloads. And yes, there will be some exciting advancements in RTX AI PCs and workstations.

Develop and Deploy on RTX

Let’s talk about RTX GPUs. These powerful graphics processing units come equipped with specialized AI hardware known as Tensor Cores, which provide the punch needed to run even the most demanding AI models. Whether you’re looking to create digital humans, chatbots, or even AI-generated podcasts, these high-performance GPUs have got you covered. With over 100 million users of Geforce RTX and nVidia RTX GPUs, developers have a vast audience ready to embrace new AI applications and features. In a session titled “Build Digital Humans, Chatbots, and AI-Generated Podcasts for RTX PCs and Workstations,” Annamalai Chockalingam, NVIDIA’s senior product manager, will walk us through an end-to-end suite of tools that make it easier than ever to develop and deploy lightning-fast AI-enabled applications.

Model Behavior: The Power of Language Models

When it comes to large language models (LLMs), the possibilities are virtually endless. They can tackle complex tasks like writing code or translating languages. But here’s the catch: while these models are trained on a wide range of knowledge, they might not be the best fit for specific tasks—like generating dialogue for non-player characters in video games. Enter small language models, which strike a balance between capability and size. They maintain accuracy while being able to run locally on more devices. In the session “Watch Your Language: Create Small Language Models That Run On-Device,” Oluwatobi Olabiyi, NVIDIA’s senior engineering manager, will share tools and techniques that developers and enthusiasts can use to generate, curate, and distill datasets, ultimately training small language models tailored for specific tasks.

Advancing Local AI Development

Building, testing, and deploying AI models on local infrastructure is becoming increasingly important for both security and performance—especially when you’re not connected to cloud services. With NVIDIA RTX GPUs leading the charge, Z by HP’s AI solutions offer the tools necessary to develop AI on-premises while keeping your data and intellectual property safe and sound. Curious about how to get started?

Get Started with NVIDIA NIM Microservices

Developers and enthusiasts can dive into AI development on RTX AI PCs and workstations thanks to NVIDIA NIM microservices. The initial public beta, which is rolling out now, includes the Llama 3.1 LLM, NVIDIA Riva Parakeet for automatic speech recognition (ASR), and YOLOX for computer vision. These microservices are not only optimized but also prepackaged models for generative AI—making it easier to download and integrate them via standard application programming interfaces.

Join Us at GTC 2025

With a keynote from NVIDIA founder and CEO Jensen Huang, over 1,000 inspiring sessions, 300+ exhibits, hands-on technical training, and a plethora of unique networking opportunities, GTC is the place to be if you’re interested in the future of AI. It’s all set to shine a spotlight on AI and its myriad benefits. So, are you ready to explore the exciting world of generative AI?

Background Information

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About HP:

Founded by Bill Hewlett and Dave Packard in a garage in 1939, HP (Hewlett-Packard) is a global technology giant known for its pioneering innovations in computing and printing technology. With a legacy of over eight decades, HP has consistently delivered a wide range of high-quality products and services, including personal computers, printers, servers, and software solutions.

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About nVidia:

NVIDIA has firmly established itself as a leader in the realm of client computing, continuously pushing the boundaries of innovation in graphics and AI technologies. With a deep commitment to enhancing user experiences, NVIDIA's client computing business focuses on delivering solutions that power everything from gaming and creative workloads to enterprise applications. for its GeForce graphics cards, the company has redefined high-performance gaming, setting industry standards for realistic visuals, fluid frame rates, and immersive experiences. Complementing its gaming expertise, NVIDIA's Quadro and NVIDIA RTX graphics cards cater to professionals in design, content creation, and scientific fields, enabling real-time ray tracing and AI-driven workflows that elevate productivity and creativity to unprecedented heights. By seamlessly integrating graphics, AI, and software, NVIDIA continues to shape the landscape of client computing, fostering innovation and immersive interactions in a rapidly evolving digital world.

  

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Technology Explained

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Geforce: Geforce is a line of graphics processing units (GPUs) developed by Nvidia. It is the most popular GPU used in the computer industry today. Geforce GPUs are used in gaming PCs, workstations, and high-end laptops. They are also used in virtual reality systems, artificial intelligence, and deep learning applications. Geforce GPUs are designed to deliver high performance and power efficiency, making them ideal for gaming and other demanding applications. They are also capable of rendering high-resolution graphics and providing smooth, realistic visuals. Geforce GPUs are used in a variety of applications, from gaming to professional workstations, and are the preferred choice for many computer users.

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LLM: A Large Language Model (LLM) is a highly advanced artificial intelligence system, often based on complex architectures like GPT-3.5, designed to comprehend and produce human-like text on a massive scale. LLMs possess exceptional capabilities in various natural language understanding and generation tasks, including answering questions, generating creative content, and delivering context-aware responses to textual inputs. These models undergo extensive training on vast datasets to grasp the nuances of language, making them invaluable tools for applications like chatbots, content generation, and language translation.

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SAN: A Storage Area Network (SAN) is a high-speed and specialized network architecture designed to facilitate the connection of storage devices, such as disk arrays and tape libraries, to servers. Unlike traditional network-attached storage (NAS), which is file-based, SAN operates at the block level, enabling direct access to storage resources. SANs are known for their performance, scalability, and flexibility, making them ideal for data-intensive applications, large enterprises, and environments requiring high availability. SANs typically employ Fibre Channel or iSCSI protocols to establish dedicated and fast communication paths between servers and storage devices. With features like centralized management, efficient data replication, and snapshot capabilities, SANs offer advanced data storage, protection, and management options. Overall, SAN technology has revolutionized data storage and management, enabling organizations to efficiently handle complex storage requirements and ensure reliable data access.

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Tensor Cores: Tensor Cores are a type of specialized hardware designed to accelerate deep learning and AI applications. They are used in the computer industry to speed up the training of deep learning models and to enable faster inference. Tensor Cores are capable of performing matrix operations at a much faster rate than traditional CPUs, allowing for faster training and inference of deep learning models. This technology is used in a variety of applications, including image recognition, natural language processing, and autonomous driving. Tensor Cores are also used in the gaming industry to improve the performance of games and to enable more realistic graphics.

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