As AI technology becomes more and more popular, there are countless ways to access these tools. Some tools can run locally on the RTX AI Pc and feature dedicated hardware for AI inference on the device for low latency and secure data processing. However, some tools need to leverage a huge amount of computing power to operate fluidly. It can be a bit confusing to understand where to turn to get the most out of the various AI applications. While some can easily run on the local hardware of RTX AI PCs, others utilize GPUs to provide quick results with minimal latency, others require so much processing that only the data center can handle them in a reasonable amount of time. NVIDIA is working on using a hybrid AI infrastructure to make it easier to find the right channels for these AI workloads. 

Hybrid AI setup allows you to run AI models in different environments and easily switch between those environments. If you start with a small project, you just need to run the model locally on your RTX AI PC, but if you want to ingest a larger data set or start more processing, you can use a hybrid AI workflow to move it to a workstation or data center, so you can adapt it to your task. You can scale up your resources by using the following methods: *********** Hybrid AI allows you to use the hardware that makes the most sense at any time, providing flexibility and scalability to your project.

NVIDIA already has many tools and technologies that work with hybrid AI systems. These tools cover a wide range of applications, including content creation, gaming, and development. 

iStock-based generative AI is one of the tools that leverage hybrid AI workflows.1 It uses models built with datasets trained with licensed content to generate, modify, style, and provide cloud-based processing to extend the canvas. Artists can process these cloud-powered works on RTX AI PCs and work locally using one of the numerous apps that support AI acceleration.

Taking a different approach, NVIDIA ACE works in the game to give the characters in the game a spark of personality and adaptive reaction, using generated AI. You can give the character a backstory, personality, etc. With significantly scalable datasets and deep customization for the vast open world, the flexibility of hybrid AI to train on a workstation or server and deploy on a local machine can help make NVIDIA ACES faster and more scalable.

NVIDIA AI Workbench leverages hybrid AI directly. This tool simplifies the process of customizing large language models (LLMs) such as Mistral-7B and Llama-2. Because each step of a project requires a variety of needs and resources, NVIDIA AI Workbench makes it easy and quick to move projects between local hardware, workstations, and cloud servers.

To keep up to date with the latest developments in AI and briefly explain complex topics, keep an eye out for NVIDIA's blog in the AI Decoded series, which explores the basics of AI and the latest tools in an easy-to-digest way. You can learn more about it here.