Nvidia corporation (20240220831). MANAGEMENT OF ARTIFICIAL INTELLIGENCE RESOURCES IN A DISTRIBUTED RESOURCE ENVIRONMENT simplified abstract
Contents
MANAGEMENT OF ARTIFICIAL INTELLIGENCE RESOURCES IN A DISTRIBUTED RESOURCE ENVIRONMENT
Organization Name
Inventor(s)
Pritish Nahar of Sunnyvale CA (US)
MANAGEMENT OF ARTIFICIAL INTELLIGENCE RESOURCES IN A DISTRIBUTED RESOURCE ENVIRONMENT - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240220831 titled 'MANAGEMENT OF ARTIFICIAL INTELLIGENCE RESOURCES IN A DISTRIBUTED RESOURCE ENVIRONMENT
The approaches presented in this patent application focus on managing artificial intelligence (AI)-related resources in a distributed environment to support accelerated machine learning applications for different users.
- An AI manager, such as a management service, is utilized to determine the requirements, capabilities, and limitations of various AI-related components, including AI models, engines, accelerators, and hardware like GPUs.
- The AI manager selects and configures resources optimized for factors such as throughput, resource utilization, and inference latency, ensuring compatibility and enforcing access control to models and data.
Potential Applications: - This technology can be applied in various industries such as healthcare, finance, and autonomous vehicles to enhance machine learning capabilities. - It can also be used in research institutions and academic settings to streamline AI resource management for different projects.
Problems Solved: - Efficient management of AI-related resources in a distributed environment. - Optimizing resource selection and configuration for specific AI models. - Ensuring compatibility and access control to models and data.
Benefits: - Improved performance and efficiency of machine learning applications. - Enhanced scalability and flexibility in managing AI resources. - Streamlined workflow for users with diverse AI needs.
Commercial Applications: AI Resource Management Technology for Enhanced Machine Learning Applications
Questions about AI Resource Management Technology: 1. How does this technology improve the efficiency of managing AI-related resources? - The technology utilizes an AI manager to optimize resource selection and configuration based on specific requirements, enhancing overall performance and efficiency.
2. What industries can benefit the most from this AI resource management technology? - Industries such as healthcare, finance, and autonomous vehicles can leverage this technology to enhance their machine learning capabilities and streamline AI resource management.
Original Abstract Submitted
approaches presented herein provide for the management of artificial intelligence (ai)-related resources in a distributed resource environment, such as may be used to support accelerated machine learning (ml) applications on behalf of different users. management functionality can be provided using an ai manager, such as a management service, that can determine the requirements, capabilities, and limitations of various available ai-related components, such as those of a plurality of ai models, engines, and accelerators, as well as the hardware (e.g., graphics processing units (gpus)) that run or make up these ai-related resources. an ai manager can determine a selection and configuration of resources that is not only appropriate for use with a specific ai model, but that can also be optimized for factors such as throughput, resource utilization, and inference latency. an ai manager can ensure compatibility of resources and configuration, and can enforce access control to models and data.
