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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. These form the core of the blog post and the most valuable content. 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. These explanations might help you get a more intuitive sense of what to look for in a GPU. 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. You can skip this section if you just want the useful performance numbers and arguments to help you decide which GPU to buy. The best high-level explanation for the question of how GPUs work is my following Quora answer. If we look at the details, we can understand what makes one GPU better than another. This section can help you build a more intuitive understanding of how to think about deep learning performance. This understanding will help you to evaluate future GPUs by yourself. It is useful to understand how they work to appreciate the importance of these computational units specialized for matrix multiplication. This is a simplified example, and not the exact way how a high performing matrix multiplication kernel would be written, but it has all the basics. To understand this example fully, you have to understand the concepts of cycles. Each cycle represents an opportunity for computation. However, most of the time, operations take longer than one cycle. Thus it creates a pipeline where for one operation to start, it needs to wait for the number of cycles of time it takes for the previous operation to finish. This is also called the latency of the operation. Furthermore, you should know that the smallest units of threads on a GPU is a pack of 32 threads this is called a warp. Warps usually operate in a synchronous pattern threads within a warp have to wait for each other. All memory operations on the GPU are optimized for warps. The resources of an SM are divided up among all active warps. For both of the following examples, we assume we have the same computational resources. A memory block in shared memory is often referred to as a memory tile or just a tile. We have 8 SMs with 8 warps each, so due to parallelization, we only need to do a single sequential load from global to shared memory, which takes cycles. To do the matrix multiplication, we now need to load a vector of 32 numbers from shared memory A and shared memory B and perform a fused multiply-and-accumulate FFMA. Then store the outputs in registers C. Why this is exactly 8 4 in older algorithms is very technical. This means we have 8x shared memory access at the cost of 20 cycles each and 8 FFMA operations 32 in parallel , which cost 4 cycles each. In total, we thus have a cost of. To do that, we first need to get memory into the Tensor Core. Similarly to the above, we need to read from global memory cycles and store in shared memory. A single SM has 8 Tensor Cores. So with 8 SMs, we have 64 Tensor Cores just the number that we need! We can transfer the data from shared memory to the Tensor Cores with 1 memory transfers 20 cycles and then do those 64 parallel Tensor Core operations 1 cycle. This means the total cost for Tensor Cores matrix multiplication, in this case, is. Thus we reduce the matrix multiplication cost significantly from cycles to cycles via Tensor Cores. While this example roughly follows the sequence of computational steps for both with and without Tensor Cores, please note that this is a very simplified example. Real cases of matrix multiplication involve much larger shared memory tiles and slightly different computational patterns. However, I believe from this example, it is also clear why the next attribute, memory bandwidth, is so crucial for Tensor-Core-equipped GPUs. Since global memory is the most considerable portion of cycle cost for matrix multiplication with Tensor Cores, we would even have faster GPUs if the global memory latency could be reduced.