NVIDIA Introduces Digital Human Microservices, Charting Path for Next-Gen AI Avatars

Computex Taipei

NVIDIA's ACE generative AI microservices simplify the process of creating, animating, and operating lifelike digital humans for various industries, with new technologies such as Audio2Gesture and the Nemotron-3 4.5B SLM being added to the platform.

The ACE generative AI microservices simplify the process of creating, animating, and operating lifelike digital humans for various applications.
The NVIDIA Nemotron-3 4.5B SLM offers the same precision and accuracy as large language models in the cloud, but can run on devices.
The AI Inference Manager SDK makes it easier to deploy ACE on PCs by preconfiguring necessary AI models and orchestrating inference across PCs and the cloud.

nVidia has announced the availability of its ACE generative AI microservices, which aim to advance the development of digital humans. These microservices will be adopted by companies in various sectors, including customer service, gaming, and healthcare. The ACE technologies simplify the process of creating, animating, and operating lifelike digital humans for applications such as customer service, telehealth, gaming, and entertainment.

One of the key products is the NVIDIA Nemotron-3 4.5B, the company’s first small language model (SLM) designed to run on devices with the same precision and accuracy as large language models (LLMs) in the cloud. The Nemotron-3 4.5B SLM is currently in early access. Additionally, NVIDIA will soon release on-device models like Audio2Face and Riva ASR for early access.

To facilitate the deployment of ACE on PCs, NVIDIA has introduced the AI Inference Manager software development kit. This SDK simplifies the process by preconfiguring the PC with the necessary AI models, engines, and dependencies, while seamlessly orchestrating AI inference across PCs and the cloud.

The suite of ACE digital human generative AI technologies includes Riva ASR, TTS, and NMT for automatic speech recognition, text-to-speech conversion, and translation. It also includes Nemotron LLM for language understanding and contextual response generation, Audio2Face for realistic facial animation based on audio tracks, and Omniverse RTX for real-time path-traced realistic skin and hair.

In addition to the existing technologies, NVIDIA has announced new additions to the ACE platform. One of these is Audio2Gesture, which generates body gestures based on audio tracks. Another is the Nemotron-3 4.5B SLM, a purpose-built small language model for low-Latency on-device RTX AI PC inference.

According to Jensen Huang, founder and CEO of NVIDIA, digital humans have the potential to revolutionize industries. The advancements in multi-modal large language models and neural graphics delivered by ACE are bringing us closer to a future where interacting with computers is as natural as interacting with humans.

NVIDIA plans to bring ACE to its installed base of 100 million RTX AI PCs and Laptops. Previously, ACE was available as NIM microservices for developers to use in data centers. Now, NVIDIA is building ACE PC NIM microservices for wider deployment.

The ACE platform has already gained traction among developers. Companies such as Aww Inc., Dell Technologies, Gumption, Hippocratic AI, Inventec, OurPalm, Perfect World Games, Reallusion, ServiceNow, Soulbotix, SoulShell, and UneeQ have adopted ACE for various applications. For instance, Aww Inc. plans to use ACE Audio2Face microservices to enhance the communication experience with its virtual celebrity, Imma. Perfect World Games is leveraging ACE for a realistic and interactive AI NPC in its game Legends. Inventec is enhancing its healthcare AI agent using ACE Audio2Face NIM for a more engaging virtual consultation experience.

At Computex, NVIDIA showcased the Covert Protocol tech demo developed in collaboration with Inworld AI. The demo allows players to interact and influence digital-human non-playable characters (NPCs) using conversational language. The demo utilizes Audio2Face and Riva ASR running locally on Geforce RTX PCs.

In summary, NVIDIA’s ACE generative AI microservices are set to accelerate the development of digital humans across various industries. With the addition of new technologies and the expansion of the ACE ecosystem, the potential for creating lifelike digital humans is becoming more accessible and seamless.

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About Dell:

Dell is a globally technology leader providing comprehensive solutions in the field of hardware, software, and services. for its customizable computers and enterprise solutions, Dell offers a diverse range of laptops, desktops, servers, and networking equipment. With a commitment to innovation and customer satisfaction, Dell caters to a wide range of consumer and business needs, making it a important player in the tech industry.

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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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Computex, held annually in Taipei, Taiwan, stands as one of the world's leading technology trade shows, showcasing cutting-edge innovations in computing hardware, software, and emerging technologies. With a focus on industry trends and product launches, it serves as a pivotal platform for tech giants and startups alike to unveil their latest advancements and forge key partnerships, attracting a global audience of industry professionals, enthusiasts, and media representatives.

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Technology Explained

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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Latency: Technology latency is the time it takes for a computer system to respond to a request. It is an important factor in the performance of computer systems, as it affects the speed and efficiency of data processing. In the computer industry, latency is a major factor in the performance of computer networks, storage systems, and other computer systems. Low latency is essential for applications that require fast response times, such as online gaming, streaming media, and real-time data processing. High latency can cause delays in data processing, resulting in slow response times and poor performance. To reduce latency, computer systems use various techniques such as caching, load balancing, and parallel processing. By reducing latency, computer systems can provide faster response times and improved performance.

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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.