Our first hypothetical startup wants to find new therapeutics by analyzing vast datasets of biomedical images from a complementary high-throughput screening program, while the second is more interested in a computational chemistry and virtual screening strategy.īoth startups want to make the world a better place by treating pathologies that have been ignored or have stymied traditional drug development, but plan to go about it in very different ways. These will be based on two different approaches to realizing a startup concerned with developing new drugs for solving neglected diseases. To make things more concrete, we’ll compare two hypothetical case studies with different approaches to similar domains and ultimately very similar prime directives. We’ll consider the main points of determining specifications for a deep learning system, including CPU for general compute, GPU (and GPU compute) for those neural network primitives, and system memory for handling large datasets. With a little diligent consideration at the outset though, you can avoid unnecessary costs and find a solution that’s optimized for both your needs and budget. It’s easy to over-engineer your system and end up paying for more capability in one area than you really need. It may not be immediately obvious at first, but the best AI hardware will depend on the type of operations you plan on running which, in turn, depends on the size and type of dataset you will be primarily working with. This article, however, is concerned with balancing hardware and computational requirements and is based on the assumption that you will be spec’ing custom AI hardware or building an AI computer yourself. In this context, “renting” would generally refer to using cloud compute resources, which tend to be more expensive in the long run, but may be a good choice in some cases (great for startups or when you’re not planning on scaling in a big way). This leaves you with an important decision: build, buy, or rent. Better late than never! Now, if you want to run machine learning, deep learning, computer vision or other AI-driven research project you can’t just buy any off-the-rack computer from an office superstore you need hardware that can handle your workload. So you’re planning to launch an AI project or startup, or maybe adding an AI-based team to an existing organization. Hardware Requirements for Artificial Intelligence Hardware Considerations When Starting an AI Project
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