![]() ![]() Fine-tuning generative AI with NVIDIA AI Workbench Enterprise AI development workflow challenges Sign up to get notified when AI Workbench is available for early access. ![]() 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.Įnterprises can connect AI Workbench to NVIDIA AI Enterprise, accelerating the adoption of generative AI and paving the way for seamless integration in production. Users can develop naturally in both JupyterLab and VS Code while managing work across a variety of machines with a high degree of reproducibility and transparency. AI Workbench integrates with platforms like GitHub, Hugging Face, and NVIDIA NGC, as well as with self-hosted registries and Git servers. AI developers choose a model, create a project within NVIDIA AI Workbench, and customize that model on their infrastructureĪfter installation, the platform provides management and deployment for containerized development environments to make sure everything works, regardless of a user’s machine. It enables developers of all levels to generate and deploy cost-effective and scalable generative AI models quickly and easily.įigure 1. Then users can scale the models to virtually any data center, public cloud, or NVIDIA DGX Cloud. NVIDIA AI Workbench is a unified, easy-to-use developer toolkit to create, test, and customize pretrained AI models on a PC or workstation. This enables seamless collaboration and deployment for developers to develop cost-effective scalable generative AI models quickly. NVIDIA AI Workbench helps simplify the process by providing a single platform for managing data, models, resources, and compute needs. The process can become incredibly complex and time consuming, especially when trying to collaborate and deploy across multiple environments and platforms. But as training jobs get larger, developers are forced to expand into additional compute infrastructure in the data center or cloud. This first step typically requires using accessible compute infrastructure, such as a PC or workstation. 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. Developing custom generative AI models and applications is a journey, not a destination. ![]()
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