Accelerating Content Generation
Build solutions to recognize, summarize, translate, predict, and generate text and visual content.
Workloads
Recommenders / Personalization
Video Streaming / Conferencing
Industries
Media and Entertainment
Retail/ Consumer Packaged Goods
Automotive / Transportation
Business Goal
Return on Investment
Products
NVIDIA AI Enterprise
NVIDIA NeMo
NVIDIA Omniverse
NVIDIA RTX Workstation
Overview
Automating Content Creation With Generative AI and Digital Twins
Generative AI and digital twins enable the rapid creation and testing of new content from a variety of multimodal inputs. Inputs and outputs of generative AI models can include text, images, video, audio, animation, 3D models, and other types of data. Alongside generative AI, digital twins provide a virtual canvas where creative concepts, assets, and environments can be modeled, tested, and evolved in real time.
With generative AI, startups and large organizations can immediately extract knowledge from their proprietary datasets. For example, you can build custom applications that speed up content generation for in-house creative teams or end customers. This can include summarizing source materials for creating new visuals or generating on-brand videos that suit your business’s narrative.
Digital twins—virtual representations of creative assets, environments, or processes—complement generative AI content creation by providing a dynamic environment for simulation, testing, and optimization. By mirroring real or conceptual objects in a digital space, digital twins allow teams to visualize, iterate, and refine content before it is finalized.
Streamlining the creative process is one key benefit. Generative AI also provides rich information to grasp underlying patterns that exist in your datasets and operations. Businesses can augment training data to reduce model bias and simulate complex scenarios. This competitive advantage fuels new opportunities to enhance content workflows, improve decision-making, and boost team efficiency in today’s fast-paced, evolving market.
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Efficiently Customize Generative AI Foundation Models
Generative AI tools powered by large language models (LLMs) show tremendous potential to transform business. To derive maximum business value, enterprises need models customized to extract insights and generate content specific to their business needs. Customizing LLMs can be an expensive, time-consuming process that requires deep technical expertise and full-stack technology investments.
For a faster, more cost-effective path to customized generative AI, enterprises are getting started with pretrained foundation models. Rather than starting from scratch, these models provide a base for enterprises to build on top of, expediting development and fine-tuning cycles while reducing costs of running and maintaining generative AI applications in production.
Technical Implementation
Developing Text-Based Content Creation Pipelines
Startups and enterprises looking to build custom generative AI models to generate context-relevant content can employ the NVIDIA AI Foundry service.
Here are the four steps to get going:
Start With State-Of-The-Art Agentic AI Models: Leading reasoning models, including Llama Nemotron, DeepSeek R1, Gemma, and Mistral, are optimized to provide highest accuracies for agentic tasks.
Customize Foundation Models: Tune and test the models with proprietary data using NVIDIA NeMo™, the end-to-end platform of microservices and SDKs for building, customizing, and deploying generative AI models anywhere.
Build Models Faster in Your Own AI Factory: Streamline AI development on NVIDIA DGX™ Cloud, a serverless AI-training-as-a-service platform for enterprise developers providing multi-node training capability and near-limitless GPU resource scale.
Deploy and Scale: Run it anywhere—cloud, data center, workstation, or edge—by deploying with NVIDIA AI Enterprise, which includes easy-to-use microservices with enterprise-grade security, support, and stability to ensure a smooth transition—from prototype to production—at scale.
NVIDIA’s AI Factory reimagines data centers as specialized systems designed to manufacture intelligence at scale. Integrating data ingestion, training, fine-tuning, and inference into one platform, it accelerates AI deployment. With NVIDIA Blackwell architecture, networking, and orchestration software, the AI Factory emphasizes efficient, token-based output, supporting generative and reasoning AI for future growth.
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Developing Visual Brand Content Generation Pipelines
Developers at independent software vendors (ISVs) and production services agencies are at the forefront of building next-gen content creation solutions, powered by controllable generative AI and built on OpenUSD. This combination allows for unprecedented flexibility and control in crafting digital content.
To achieve this, developers are carefully selecting state-of-the-art generative AI foundation models—including those from Google, Mixtral, Meta, Stability AI, NVIDIA, and more— based on their performance-per-dollar benchmarks and architecture suitability for multimodal content generation tasks. Models are fine-tuned and evaluated using proprietary datasets within NVIDIA NeMo, which supports supervised and reinforcement-based techniques for model adaptation, prompt tuning, and evaluation at scale.
Training and experimentation are executed on NVIDIA DGX Cloud, enabling elastic, multi-node distributed training across high-performance GPU clusters without infrastructure overhead. This allows for rapid iteration cycles, high-throughput experimentation, and integration with existing MLOps pipelines.
Deployment is containerized and managed through NVIDIA AI Enterprise, offering production-grade inference microservices, observability tooling, and hardened security. The stack supports hybrid and multi-cloud topologies, enabling seamless inference deployment across cloud, on-prem, workstation, or edge environments while maintaining model integrity and operational resilience.
FAQs
Digital twins are physically accurate, real-time virtual replicas of objects, processes, or environments, built on OpenUSD and powered by AI through NVIDIA Omniverse.
To get started, explore NVIDIA Deep Learning Institute's "Building a 3D Product Configurator with USD and Omniverse" course and access detailed product configurator documentation.
You can also dive into creating OpenUSD applications for various industries with the "How to Build OpenUSD Applications for Industrial Digital Twins" course.
NVIDIA Omniverse uses OpenUSD to integrate with asset management systems and maintain a single source of truth for brand assets. This helps ensure that all generated visuals—whether 2D, 3D, or video—adhere to brand guidelines, product specifications, and visual standards. AI models are trained on brand-approved data, and AI tools like USD Search and USD Code NIM microservices help teams access and assemble only approved assets. Use the 3D Conditioning for Precise Visual Generative AI Blueprint to get started.
NVIDIA Omniverse—powered by generative AI and OpenUSD—enables developers to build tools for scalable, personalized content creation. Key features include:
- Modular Asset Management: Developers can create systems where digital assets (like products or environments) are easily swapped or customized for different audiences.
- Generative AI Integration: The platform supports AI-driven asset and scene generation, allowing for rapid content variation based on campaign needs.
- Real-Time Customization: Developers can easily enable interactive scene editing and automation with Omniverse, making it easier to tailor visuals for specific demographics.
- Scalability: The platform supports high-volume, efficient production of unique content variants, empowering brands to reach diverse market segments.
In the world of LLMs, choosing between fine-tuning, Parameter-Efficient Fine-Tuning (PEFT), prompt engineering, and retrieval-augmented generation (RAG) depends on the specific needs and constraints of your application.
- Fine-tuning customizes a pretrained LLM for a specific domain by updating most or all of its parameters with a domain-specific dataset. This approach is resource-intensive but yields high accuracy for specialized use cases.
- PEFT modifies a pretrained LLM with fewer parameter updates, focusing on a subset of the model. It strikes a balance between accuracy and resource usage, offering improvements over prompt engineering with manageable data and computational demands.
- Prompt engineering manipulates the input to an LLM to steer its output, without altering the model’s parameters. It’s the least resource-intensive method, suitable for applications with limited data and computational resources.
- RAG enhances LLM prompts with information from external databases—effectively, a sophisticated form of prompt engineering. RAG enables access to the most up-to-date, real-time information from the most relevant sources.
There are several frameworks for connecting LLMs to your data sources, such as LangChain and LlamaIndex. These frameworks provide a variety of features, like evaluation libraries, document loaders, and query methods. New solutions are also coming out all the time. We recommend reading about various frameworks and picking the software and components of the software that make the most sense for your application.
Yes. With RAG, the most recent relevant information, including references for retrieved data, are provided.
NVIDIA AI workflow examples accelerate the building and deploying of enterprise solutions with RAG. With our GitHub examples, write RAG applications using the latest GPU-optimized LLMs and NVIDIA NeMo microservices.
Digital twins are physically accurate, real-time virtual replicas of objects, processes, or environments, built on OpenUSD and powered by AI through NVIDIA Omniverse.
To get started, explore NVIDIA Deep Learning Institute's "Building a 3D Product Configurator with USD and Omniverse" course and access detailed product configurator documentation.
You can also dive into creating OpenUSD applications for various industries with the "How to Build OpenUSD Applications for Industrial Digital Twins" course.
Additional resources on Omniverse and digital twin development are available through NVIDIA's comprehensive documentation and within OpenUSD courses.
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