Which GPU(s) to Get for Deep Learning

Deep learning is a field with intense computational requirements, and your choice of GPU will fundamentally determine your deep learning experience. But what features are important if you want to buy a new GPU? How to make a cost-efficient choice? This blog post will delve into these questions, tackle common misconceptions, give you an intuitive understanding of how to think about GPUs, and will lend you advice, which will help you to make a choice that is right for you. This blog post is designed to give you different levels of understanding about GPUs and the new Ampere series GPUs from NVIDIA. You have the choice: (1) If you are not interested in the details of how GPUs work, what makes a GPU fast, and what is unique about the new NVIDIA RTX 30 Ampere series, you can skip right to the performance and performance per dollar charts and the recommendation section. You might want to skip a section or two based on your understanding of the presented topics. I will head each major section with a small summary, which might help you to decide if you want to read the section or not. This blog post is structured in the following way. First, I will explain what makes a GPU fast. I will discuss CPUs vs GPUs, Tensor Cores, memory bandwidth, and the memory hierarchy of GPUs and how these relate to deep learning performance. These explanations might help you to get a more intuitive sense of what to look for in a GPU. Then I will make theoretical estimates for GPU performance and align them with some marketing benchmarks from NVIDIA to get reliable, unbiased performance data. I discuss the unique features of the new NVIDIA RTX 30 Ampere GPU series that are worth considering if you buy a GPU. From there, I make GPU recommendations for 1-2, 4, 8 GPU setups, and GPU clusters. After that follows a Q&A section of common questions posed to me in Twitter threads; in that section, I will also address common misconceptions and some miscellaneous issues, such as cloud vs desktop, cooling, AMD vs NVIDIA, and others. If you use GPUs frequently, it is useful to understand how they work. This knowledge will come in handy in understanding why GPUs might be slow in some cases and fast in others. In turn, you might be able to understand better why you need a GPU in the first place and how other future hardware options might be able to compete.