I discuss the unique features of the new NVIDIA RTX 40 Ampere GPU series that are worth considering if you buy a GPU. These explanations might help you get a more intuitive sense of what to look for in a GPU. I will discuss CPUs vs GPUs, Tensor Cores, memory bandwidth, and the memory hierarchy of GPUs and how these relate to deep learning performance. First, I will explain what makes a GPU fast. This blog post is structured in the following way. You might want to skip a section or two based on your understanding of the presented topics. (3) If you want to get an in-depth understanding of how GPUs, caches, and Tensor Cores work, the best is to read the blog post from start to finish. (2) If you worry about specific questions, I have answered and addressed the most common questions and misconceptions in the later part of the blog post. The cost/performance numbers form the core of the blog post and the content surrounding it explains the details of what makes up GPU performance. You have the choice: (1) If you are not interested in the details of how GPUs work, what makes a GPU fast compared to a CPU, and what is unique about the new NVIDIA RTX 40 Ampere series, you can skip right to the performance and performance per dollar charts and the recommendation section. This blog post is designed to give you different levels of understanding of GPUs and the new Ampere series GPUs from NVIDIA.
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But what features are important if you want to buy a new GPU? GPU RAM, cores, tensor cores, caches? 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. Deep learning is a field with intense computational requirements, and your choice of GPU will fundamentally determine your deep learning experience.
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