Better 3D Meshes, from Reconstruction to Generative AI NVIDIA Technical Blog

Generative AI Opens New Era of Efficiency Across Industries NVIDIA Blog

And with the latest generation of RTX laptops and mobile workstations built on the NVIDIA Ada Lovelace architecture, users can take generative AI anywhere. Our next-gen mobile platform brings new levels of performance and portability — in form factors as small as 14 inches and as lightweight as about three pounds. Makers like Dell, HP, Lenovo and ASUS are pushing the generative AI era forward, backed by RTX GPUs and Tensor Cores. Check out this new ebook on practical applications and thoughts on future generative AI developments. The user uses a text prompt to generate a desired image and selects a style prompt, and their image is generated within seconds.

nvidia generative ai

Developing custom generative AI models and applications is a journey, not a destination. It begins with selecting a pretrained model, such as a Large Language Model, for exploratory purposes—then developers often want to tune that model for their specific use case. This first step typically requires using accessible compute infrastructure, such as a PC or workstation.

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Available everywhere, NVIDIA AI Enterprise gives organizations the flexibility to run their NVIDIA AI-enabled solutions in the cloud, data center, workstations, and at the edge—develop once, deploy anywhere. With NVIDIA BioNeMo™, researchers and developers can use generative AI models to rapidly generate the structure and function of proteins and molecules, accelerating the creation of new drug candidates. The computer-generated voice is helpful to develop video voiceovers, audible clips, and narrations for companies and individuals. AI is used in extraordinary ways to process low-resolution images and develop more precise, clearer, and detailed pictures. For example, Google published a blog post to let the world know they have created two models to turn low-resolution images into high-resolution images. This new tech in AI determines the original pattern entered in the input to generate creative, authentic pieces that showcase the training data features.

Developers can then move seamlessly to the cloud to train on the same NVIDIA AI stack, which is available from every major cloud service provider. Next, developers can optimize the trained models for fast inferencing with tools like the new Microsoft Olive. And finally, they can deploy their AI-enabled applications and features to an install base of over 100 million RTX PCs and workstations  that have been optimized for AI. A preeminent global visual content creator and marketplace, Getty Images is working in collaboration with NVIDIA to provide custom developed image and video generation models on Picasso, trained on fully licensed data. Built on the platform, NVIDIA AI foundries are equipped with generative model architectures, tools, and accelerated computing for training, customizing, optimizing, and deploying generative AI.

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It uses GPUs, DPUs and networking along with CPUs to accelerate applications across science, analytics, engineering, as well as consumer and enterprise use cases. Many of the products and features described herein remain in various stages and will be offered on a when-and-if-available basis. NVIDIA will have no liability for failure to deliver or delay in the delivery of any of the products, features or functions set forth herein. NVIDIA BlueField-3 DPUs accelerate, genrative ai offload and isolate the tremendous compute load of virtualization, networking, storage, security and other cloud-native AI services from the GPU or CPU. The NVIDIA L40S GPU enables up to 1.2x more generative AI inference performance and up to 1.7x more training performance compared with the NVIDIA A100 Tensor Core GPU. To meet that need, Google Cloud today announced the general availability of its new A3 instances, powered by NVIDIA H100 Tensor Core GPUs.

Founder of the DevEducation project

  • Developers with an NVIDIA RTX PC or workstation can also launch, test, and fine-tune enterprise-grade generative AI projects on their local systems, and access data center and cloud computing resources when scaling up.
  • If you’d like to retain your premium access and save 20%, you can opt to pay annually at the end of the trial.
  • For example, AI algorithms can learn from web activity and user data to interpret customers’ opinions towards a company and its products or services.

Adobe and NVIDIA will co-develop generative AI models with a focus on responsible content attribution and provenance to accelerate workflows of the world’s leading creators and marketers. These models will be jointly developed and brought to market through Adobe Cloud flagship products like Photoshop, Premiere Pro, and After Effects, as well as through Picasso. Simplify development with a suite of model-making services, pretrained models, cutting-edge frameworks, and APIs. This first wave of Generative AI applications resembles the mobile application landscape when the iPhone first came out—somewhat gimmicky and thin, with unclear competitive differentiation and business models. However, some of these applications provide an interesting glimpse into what the future may hold.

It took the team just two days to train the model on around 1 million images using NVIDIA A100 Tensor Core GPUs. The generated objects could be used in 3D representations of buildings, outdoor spaces or entire cities, designed for industries including gaming, robotics, architecture and social media. NeMo-powered LLM generates responses based on real-time information from the company’s database. For instance, GSC Game World is using Audio2Face in the much-anticipated S.T.A.L.K.E.R. 2 Heart of Chornobyl. And indie developer Fallen Leaf is using Audio2Face for character facial animation in Fort Solis, their third-person sci-fi thriller set on Mars. Additionally,, a company enabling virtual characters through AI, is leveraging Audio2Face to power the animation in their conversation engine.

Organizations can focus on harnessing the game-changing insights of AI, instead of maintaining and tuning their AI development platform. TensorRT-LLM is built on the FasterTransformer project, with improved flexibility and closer pairing with NVIDIA Triton Inference Server for greater end-to-end performance on state-of-the-art LLMs. Several businesses already use automated fraud-detection practices that leverage the power of AI. These practices have helped them locate malicious and suspicious actions quickly and with superior accuracy.

Image processing

It will take time to build these applications the right way to accumulate users and data, but we believe the best ones will be durable and have a chance to become massive. Generative AI gives users the ability to quickly generate new content, such as text, images, sounds, animation, 3D models, and computer code. Tapping into knowledge base question answering (KBQA) powered by generative AI, chatbots can accurately answer domain-specific questions by retrieving information from a company’s knowledge base and providing real-time responses in natural language. For deploying generative AI in production, NeMo uses TensorRT for Large Language Models (TRT-LLM), which accelerates and optimizes inference performance on the latest LLMs on NVIDIA GPUs.

nvidia generative ai

Generative AI models can take inputs such as text, image, audio, video, and code and generate new content into any of the modalities mentioned. For example, it can turn text inputs into an image, turn an image into a song, or turn video into text. The NVIDIA Developer Program provides access to hundreds of software and performance analysis tools across diverse industries and use cases. Join the program to get access to generative AI tools, technical training, documentation, how-to guides, technical experts, developer forums, and more.