Accelerating Content Generation

Generative AI can recognize, summarize, translate, predict, and generate text and other forms of content.

Workloads

Generative AI

Business Goal

Return on Investment

Products

NVIDIA NeMo
NVIDIA Picasso
NVIDIA AI Enterprise

Automating Content Creation With Generative AI

Generative AI is a powerful branch of artificial intelligence that holds immense potential for solving various challenges faced by organizations worldwide. It can quickly create new content based on a variety of multi-modal inputs. Inputs and outputs to these models can include text, images, video, audio, animation, 3D models, or other types of data. 

​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.

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 your existing creative workflows, improve decision-making, and boost team efficiency in today’s fast-paced, evolving market.

Temporal layers and novel video denoiser generate high-fidelity videos with temporal consistency.

Efficiently Customize Generative AI Foundation Models

While generative AI tools powered by large language models (LLMs)  show tremendous promise, and to derive maximum business value, enterprises need models customized to extract insights and generate content specific to their business needs. Customizing large language models (LLMs) can be an expensive, time-consuming process that requires deep technical expertise and full-stack technology investments. LLMs are used to accelerate numerous applications, including AI chatbots for online shopping, banking assistants, writing assistants, translation tools, and AI for predicting protein structures in biomedical research.

For a faster, more cost-effective path, to customized generative AI, enterprises are getting started with pre-trained foundation models. Rather than starting from scratch, these models provide a base for enterprises to build on top of, resulting in expedited development and fine-tuning cycles, and significant cost savings when running and maintaining generative AI applications in production.

 

Startup Pens Success Using NVIDIA AI

Using NVIDIA AI software, ‘Writer’ builds LLMs that are helping hundreds of companies create content.

With NVIDIA NeMo, organizations can curate their training datasets, build and customize LLMs, and run them in production at scale. Organizations everywhere from Korea to Sweden are using it to customize LLMs for their local languages and industries.

A startup called Writer started using NeMo to put generative AI to work and create content for hundreds of companies. They claimed that before working with NVIDIA, it took them four and a half months to build a new billion-parameter model. “Now we can do it in 16 days—this is mind-blowing,” Alshikh (CEO of Writer) said. Hundreds of businesses now use Writer’s models that NeMo customized for finance, healthcare, retail, and other vertical markets.

NeMo is a part of NVIDIA AI Enterprise—full-stack software optimized to accelerate generative AI workloads and backed by enterprise-grade support, security, and application programming interface stability.

“Before NeMo, it took us four and a half months to build a new billion-parameter model.  Now we can do it in 16 days—this is mind blowing.“

Waseeem Alshikh
CTO, Writer.ai

Leveraging Generative AI for Content Creation

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:

  1. Start With State-of-the-Art Generative AI Models: Leading foundation models include Gemma 7B, Mixtral 8x7B Instruct,  Llama 2 70B, Stable Diffusion XL, and NVIDIA’s Nemotron-3 8B family, optimized for the highest performance per cost. 

  2. Customize Foundation Models: Tune and test the models with proprietary data using NVIDIA NeMo, the end-to-end cloud-native framework for building, customizing, and deploying generative AI models anywhere. You may also customize commercially safe NVIDIA Edify foundation models for visual content using NVIDIA Picasso. 

  3. 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. 

  4. Deploy and Scale: Run it anywhere, cloud, data center, workstation, or edge, by deploying with NVIDIA AI Enterprise that includes easy-to-use microservices with enterprise-grade security, support, and stability to ensure a smooth transition—from prototype to production—at scale.

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 a variety of 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. The NVIDIA RAG LLM operator, part of NVIDIA AI Enterprise, deploys RAG pipelines developed using the example workflows into production without rewriting any code.

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