Nvidia has recently introduced the AI Workbench, a platform designed to simplify and streamline the creation of generative AI models. This workspace enables developers to develop and deploy such models on various Nvidia AI platforms, including PCs and workstations. The AI Workbench aims to make the process of creating AI content more accessible to a wider audience.
The announcement from Nvidia acknowledges the existence of numerous pretrained models, but emphasizes the time and effort required to customize them. This is where the AI Workbench comes in, providing a simplified approach. Developers can now customize and run generative AI with minimal effort, utilizing enterprise-grade models. The Workbench tool also supports various frameworks, libraries, and SDKs from Nvidia’s AI platform, as well as open-source repositories like GitHub and Hugging Face.
Once customized, these models can be easily shared across multiple platforms. Developers using PCs or workstations with Nvidia RTX graphics cards can work with these models locally and also scale up to data center and cloud computing resources as needed.
“Nvidia AI Workbench provides a simplified path for cross-organizational teams to create AI-based applications that are increasingly essential in modern business,” said Manuvir Das, Nvidia’s vice president of enterprise computing.
In addition to the AI Workbench, Nvidia has also announced the fourth iteration of its Nvidia AI Enterprise software platform. This platform offers the necessary tools to adopt and customize generative AI models. One of the tools included is Nvidia NeMo, a cloud-native framework that allows users to build and deploy large language models (LLMs) like ChatGPT or Google Bard.
Nvidia is actively venturing into the AI market, leveraging technologies like the AI Workbench and Nvidia ACE for games. With the increasing popularity of generative AI models like ChatGPT, many developers may find Nvidia’s comprehensive solution appealing. However, the implications of widespread adoption and training of these models remain to be seen, as generative AI can exhibit unpredictable behavior. Nonetheless, the AI Workbench has the potential to simplify the deployment of new generative AI models for many companies.
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