# 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](https://www.nvidia.com/en-us/glossary/generative-ai.md) and [digital twins](https://www.nvidia.com/en-us/glossary/digital-twin.md) 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. ### 3D-Guided Generative AI Blueprint Design scenes in 3D to direct image layout, then refine and swap assets with generative AI while preserving your core composition and simply changing the camera for new perspectives. [Learn More](https://build.nvidia.com/nvidia/genai-3d-guided) Quick Links [Read Blog: Retail Brands Transform Marketing](https://blogs.nvidia.com/blog/retail-agentic-physical-ai/) [Learn More About NVIDIA NeMo](https://www.nvidia.com/en-us/ai-data-science/generative-ai/nemo-framework.md) [Learn More About 3D Guided Generative AI](https://build.nvidia.com/nvidia/genai-3d-guided) ## Efficiently Customize Generative AI Foundation Models Generative AI tools powered by [large language models](https://www.nvidia.com/en-us/glossary/large-language-models.md) (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](https://www.nvidia.com/en-us/ai/foundry.md) service. Here are the four steps to get going: 1. **Start With State-Of-The-Art Agentic AI Models**: Leading reasoning models, including [Llama Nemotron](https://build.nvidia.com/search?q=nemotron), [DeepSeek R1](https://catalog.ngc.nvidia.com/orgs/nim/teams/deepseek-ai/containers/deepseek-r1), [Gemma](https://build.nvidia.com/search?q=gemma-3), and [Mistral](https://build.nvidia.com/mistralai/mistral-nemotron), are optimized to provide highest accuracies for agentic tasks. 2. **Customize Foundation Models**: Tune and test the models with proprietary data using [NVIDIA NeMo](https://www.nvidia.com/en-us/ai-data-science/generative-ai/nemo-framework.md)™, the end-to-end platform of microservices and SDKs for building, customizing, and deploying generative AI models anywhere. 3. **Build Models Faster in Your Own AI Factory**: Streamline AI development on [NVIDIA DGX™ Cloud](https://www.nvidia.com/en-us/data-center/dgx-cloud.md), a serverless AI-training-as-a-service platform for enterprise developers providing multi-node training capability and near-limitless GPU resource scale. 4. **Deploy and Scale**: Run it anywhere—cloud, data center, workstation, or edge—by deploying with [NVIDIA AI Enterprise](https://www.nvidia.com/en-us/data-center/products/ai-enterprise.md), 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](https://www.nvidia.com/en-us/data-center/technologies/blackwell-architecture.md), networking, and orchestration software, the AI Factory emphasizes efficient, token-based output, supporting generative and reasoning AI for future growth. Quick Links [How to Create a Custom Language Model](https://developer.nvidia.com/blog/how-to-create-a-custom-language-model/) [Commercially Safe Custom Models for Visual Design](https://www.nvidia.com/en-eu/gpu-cloud/picasso.md) [3D Product Configurator Reference Architecture](https://resources.nvidia.com/en-us-omniverse-product-configurator/blueprint-3d-conditioning) ## 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](https://blogs.nvidia.com/blog/retail-agentic-physical-ai/), powered by controllable generative AI and built on [OpenUSD](https://www.nvidia.com/en-us/omniverse/usd.md). 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 ### How do I get started generating content with digital twins? 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](https://resources.nvidia.com/en-us-omniverse-product-configurator/course-detail?xs=662847) and access detailed [product configurator documentation](https://docs.omniverse.nvidia.com/extensions/latest/ext_product-configurator.html). You can also dive into creating OpenUSD applications for various industries with the ["How to Build OpenUSD Applications for Industrial Digital Twins" course](https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-OV-13+V1). ### How do you ensure brand accuracy and consistency in generated visuals? 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](https://build.nvidia.com/nvidia/conditioning-for-precise-visual-generative-ai) to get started. ### How can retailers and advertisers personalize content for different audiences with AI? 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. ### When should you fine-tune the LLM vs. use RAG? In the world of LLMs, choosing between fine-tuning, Parameter-Efficient Fine-Tuning (PEFT), prompt engineering, and [retrieval-augmented generation](https://www.nvidia.com/en-us/glossary/retrieval-augmented-generation.md) (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. ### How do you connect LLMs to data 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. ### Can a RAG system reference sources for data that it retrieves? Yes. With RAG, the most recent relevant information, including references for retrieved data, are provided. ### What’s the best way to get started in building a RAG application? [NVIDIA AI workflow examples](https://www.nvidia.com/en-us/ai-data-science/ai-workflows/generative-ai-chatbots.md) accelerate the building and deploying of enterprise solutions with RAG. With our [GitHub examples](https://nam11.safelinks.protection.outlook.com/?url=https%3A%2F%2Fnvidia.github.io%2FGenerativeAIExamples%2Flatest&data=05%7C02%7Cnsessions%40nvidia.com%7C41670678aa944d57fc6208dc4451d005%7C43083d15727340c1b7db39efd9ccc17a%7C0%7C0%7C638460365395878559%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=EiN1%2B5xV0FHGIT%2FFDGd0wIrI2cgBzSYLMbAWIaWK6Dc%3D&reserved=0), write RAG applications using the latest GPU-optimized LLMs and NVIDIA NeMo microservices. ### How do I get started generating content with digital twins? Digital twins are physically accurate, real-time virtual replicas of objects, processes, or environments, built on [OpenUSD](https://www.nvidia.com/en-us/omniverse/usd.md) and powered by AI through [NVIDIA Omniverse](https://www.nvidia.com/en-us/omniverse.md). To get started, explore NVIDIA Deep Learning Institute's ["Building a 3D Product Configurator with USD and Omniverse" course](https://resources.nvidia.com/en-us-omniverse-product-configurator/course-detail?xs=662847) and access detailed [product configurator documentation](https://docs.omniverse.nvidia.com/extensions/latest/ext_product-configurator.html). You can also dive into creating OpenUSD applications for various industries with the ["How to Build OpenUSD Applications for Industrial Digital Twins" course](https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-OV-13+V1). Additional resources on Omniverse and digital twin development are available through [NVIDIA's comprehensive documentation](https://docs.omniverse.nvidia.com/digital-twins/latest/index.html) and within [OpenUSD courses](https://www.nvidia.com/en-us/learn/learning-path/openusd.md). Quick Links [What is Retrieval-Augmented Generation?](https://blogs.nvidia.com/blog/what-is-retrieval-augmented-generation/) [What is a Digital Twin?](https://blogs.nvidia.com/blog/what-is-retrieval-augmented-generation/) [Build AI Chatbots Using RAG Workflows](https://www.nvidia.com/en-eu/ai-data-science/ai-workflows/generative-ai-chatbots.md) ### Build a Content Generation Pipeline [Contact Sales](https://www.nvidia.com/en-eu/data-center/products/ai-enterprise/contact-sales.md) [Get Started](https://build.nvidia.com/nvidia/conditioning-for-precise-visual-generative-ai) ## News Load More ## Related Use Cases