NVIDIA announced today that leading AI application developers across a wide range of industries are using NVIDIA digital human technologies to create lifelike avatars for commercial applications and dynamic game characters. The results are on display at GTC, the global AI conference held this week in San Jose, Calif., and can be seen in technology demonstrations from Hippocratic AI, Inworld AI, UneeQ and more.

NVIDIA Avatar Cloud Engine (ACE) for speech and animation, NVIDIA NeMo for language, and NVIDIA RTX for ray-traced rendering are the building blocks that enable developers to create digital humans capable of AI-powered natural language interactions, making conversations more realistic and engaging.

2024 will be the year generative AI gets personal, the CEOs of NVIDIA and HP said today in a fireside chat, unveiling new laptops that can build, test and run large language models. "This is a renaissance of the personal computer," said NVIDIA founder and CEO Jensen Huang at HP Amplify, a gathering in Las Vegas of about 1,500 resellers and distributors. "The work of creators, designers and data scientists is going to be revolutionized by these new workstations."

Greater Speed and Security
"AI is the biggest thing to come to the PC in decades," said HP's Enrique Lores, in the runup to the announcement of what his company billed as "the industry's largest portfolio of AI PCs and workstations." Compared to running their AI work in the cloud, the new systems will provide increased speed and security while reducing costs and energy, Lores said in a keynote at the event. New HP ZBooks provide a portfolio of mobile AI workstations powered by a full range of NVIDIA RTX Ada Generation GPUs. Entry-level systems with the NVIDIA RTX 500 Ada Generation Laptop GPU let users run generative AI apps and tools wherever they go. High-end models pack the RTX 5000 to deliver up to 682 TOPS, so they can create and run LLMs locally, using retrieval-augmented generation (RAG) to connect to their content for results that are both personalized and private.

ServiceNow, Hugging Face, and NVIDIA today announced the release of StarCoder2, a family of open-access large language models for code generation that sets new standards for performance, transparency, and cost-effectiveness. StarCoder2 was developed in partnership with the BigCode Community, managed by ServiceNow, the leading digital workflow company making the world work better for everyone, and Hugging Face, the most-used open-source platform, where the machine learning community collaborates on models, datasets, and applications. Trained on 619 programming languages, StarCoder2 can be further trained and embedded in enterprise applications to perform specialized tasks such as application source code generation, workflow generation, text summarization, and more. Developers can use its code completion, advanced code summarization, code snippets retrieval, and other capabilities to accelerate innovation and improve productivity.

StarCoder2 offers three model sizes: a 3-billion-parameter model trained by ServiceNow; a 7-billion-parameter model trained by Hugging Face; and a 15-billion-parameter model built by NVIDIA with NVIDIA NeMo and trained on NVIDIA accelerated infrastructure. The smaller variants provide powerful performance while saving on compute costs, as fewer parameters require less computing during inference. In fact, the new 3-billion-parameter model matches the performance of the original StarCoder 15-billion-parameter model. "StarCoder2 stands as a testament to the combined power of open scientific collaboration and responsible AI practices with an ethical data supply chain," emphasized Harm de Vries, lead of ServiceNow's StarCoder2 development team and co-lead of BigCode. "The state-of-the-art open-access model improves on prior generative AI performance to increase developer productivity and provides developers equal access to the benefits of code generation AI, which in turn enables organizations of any size to more easily meet their full business potential."

NVIDIA (NASDAQ: NVDA) today reported revenue for the fourth quarter ended January 28, 2024, of $22.1 billion, up 22% from the previous quarter and up 265% from a year ago. For the quarter, GAAP earnings per diluted share was $4.93, up 33% from the previous quarter and up 765% from a year ago. Non-GAAP earnings per diluted share was $5.16, up 28% from the previous quarter and up 486% from a year ago.

For fiscal 2024, revenue was up 126% to $60.9 billion. GAAP earnings per diluted share was $11.93, up 586% from a year ago. Non-GAAP earnings per diluted share was $12.96, up 288% from a year ago. "Accelerated computing and generative AI have hit the tipping point. Demand is surging worldwide across companies, industries and nations," said Jensen Huang, founder and CEO of NVIDIA.

Amazon Web Services, Inc. (AWS), an Amazon.com, Inc. company (NASDAQ: AMZN), and NVIDIA (NASDAQ: NVDA) today announced an expansion of their strategic collaboration to deliver the most-advanced infrastructure, software and services to power customers' generative artificial intelligence (AI) innovations. The companies will bring together the best of NVIDIA and AWS technologies—from NVIDIA's newest multi-node systems featuring next-generation GPUs, CPUs and AI software, to AWS Nitro System advanced virtualization and security, Elastic Fabric Adapter (EFA) interconnect, and UltraCluster scalability—that are ideal for training foundation models and building generative AI applications.

The expanded collaboration builds on a longstanding relationship that has fueled the generative AI era by offering early machine learning (ML) pioneers the compute performance required to advance the state-of-the-art in these technologies.

Today, Dropbox, Inc. and NVIDIA announced a collaboration to supercharge knowledge work and improve productivity for millions of Dropbox customers through the power of AI. The companies' collaboration will expand Dropbox's extensive AI functionality with new uses for personalized generative AI to improve search accuracy, provide better organization, and simplify workflows for its customers across their cloud content.

Dropbox plans to leverage NVIDIA's AI foundry consisting of NVIDIA AI Foundation Models, NVIDIA AI Enterprise software and NVIDIA accelerated computing to enhance its latest AI-powered product experiences. These include Dropbox Dash, universal search that connects apps, tools, and content in a single search bar to help customers find what they need; Dropbox AI, a tool that allows customers to ask questions and get summaries on large files across their entire Dropbox; among other AI capabilities in Dropbox.

NVIDIA today introduced an AI foundry service to supercharge the development and tuning of custom generative AI applications for enterprises and startups deploying on Microsoft Azure.

The NVIDIA AI foundry service pulls together three elements—a collection of NVIDIA AI Foundation Models, NVIDIA NeMo framework and tools, and NVIDIA DGX Cloud AI supercomputing services—that give enterprises an end-to-end solution for creating custom generative AI models. Businesses can then deploy their customized models with NVIDIA AI Enterprise software to power generative AI applications, including intelligent search, summarization and content generation.

NVIDIA's AI platform raised the bar for AI training and high performance computing in the latest MLPerf industry benchmarks. Among many new records and milestones, one in generative AI stands out: NVIDIA Eos - an AI supercomputer powered by a whopping 10,752 NVIDIA H100 Tensor Core GPUs and NVIDIA Quantum-2 InfiniBand networking - completed a training benchmark based on a GPT-3 model with 175 billion parameters trained on one billion tokens in just 3.9 minutes. That's a nearly 3x gain from 10.9 minutes, the record NVIDIA set when the test was introduced less than six months ago.

The benchmark uses a portion of the full GPT-3 data set behind the popular ChatGPT service that, by extrapolation, Eos could now train in just eight days, 73x faster than a prior state-of-the-art system using 512 A100 GPUs. The acceleration in training time reduces costs, saves energy and speeds time-to-market. It's heavy lifting that makes large language models widely available so every business can adopt them with tools like NVIDIA NeMo, a framework for customizing LLMs. In a new generative AI test ‌this round, 1,024 NVIDIA Hopper architecture GPUs completed a training benchmark based on the Stable Diffusion text-to-image model in 2.5 minutes, setting a high bar on this new workload. By adopting these two tests, MLPerf reinforces its leadership as the industry standard for measuring AI performance, since generative AI is the most transformative technology of our time.