Hardware Meta Guide – Meta-Guide.com

Notes:

There are several characteristics that are important to consider when selecting computer hardware for use in artificial intelligence (AI) applications:

Processing power: AI algorithms often require significant processing power to perform tasks such as training machine learning models or processing large amounts of data in real-time. Therefore, it is important to choose hardware with a fast processor and sufficient memory to handle the demands of the AI application.
GPU support: Graphics processing units (GPUs) are specialized chips designed to handle the high computational demands of tasks such as image and video processing. Many AI applications, particularly those that involve deep learning, can benefit from the use of GPUs to accelerate the training and inference process.
Storage capacity: AI applications often require large amounts of data for training and testing, and it is important to have sufficient storage capacity to store this data. Hard disk drives (HDDs) and solid-state drives (SSDs) are both options for storing data, with SSDs generally being faster but more expensive.
Network connectivity: If the AI application involves data processing or communication over a network, it is important to consider the hardware’s network connectivity options. This may include Ethernet, Wi-Fi, or mobile network connectivity.
Robustness and reliability: AI applications often require continuous operation, so it is important to choose hardware that is reliable and able to withstand the demands of the application.

Resources:

digitalocean.com .. cloud computing platform of virtual servers

Wikipedia:

References:

Life inside of China’s massive and remote bitcoin mines (12 Jul 2017)
Cloud Computing for Enterprise Architectures (2011)
Middleware and Cloud Computing (2011)
Moving To The Cloud (2011)