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.
The GPU Technology Conference is the most exciting event for the AI and ML ecosystem. From researchers in academia to product managers at hyperscale cloud companies to IoT builders and makers, this conference has something relevant for each of them. As an AIoT enthusiast and a maker, I eagerly look forward to GTC. Due to the current COVID-19 situation, I was a bit disappointed to see the event turning into a virtual conference. But the keynote delivered by Jensen Huang, the CEO of NVIDIA made me forget that it was a virtual event.
I think I am going to echo what has been said. Windoze is not going to cut it. I know you don't want to do Linux for whatever reason; I am/was hardcore Mac and really wanted to find a way to do GPU work in MacOS. I'm now looking at building an all AMD machine with POP! just as a test bed for the ROCm stuff; I am just quirky that way. So, you're a bit ahead of me; I had to buy a PC and then put Linux on it (I deleted Windoze before I even started).
GPUs are designed and sized to run some of the most complex deep learning models such as RESNET, NMT, Transformer, DeepSpeech, and NCF. Most enterprise models being trained or deployed use only a fraction of the GPU compute and memory capacity. So, how do you reclaim this memory and compute headroom so that you can get the most out of your GPU investment? Watson Machine Learning Accelerator provides facilities to share GPU resources across multiple small jobs. This allows maximal return-on-investment for IT teams in enterprises where GPUs are in high demand. Additionally, you benefit from sharing a GPU across multiple jobs when your jobs are waiting for GPU resources or your distributed jobs running across GPUs might be stacked on top of each other on as few GPUs as possible to reduce the execution footprint.
For almost two decades, GPUs (Graphics Processing Units) have been steadily revolutionizing high-performance computing (HPC) and AI. Originally designed for graphics-intensive applications such as gaming and image processing, it didn't take long for HPC professionals to see the potential of low-cost, massively parallel processors able to handle then billions (and now trillions) of floating-point operations per second. In this two-part article, I'll discuss GPU workloads and how they are managed with Univa Grid Engine. First, I'll provide a short primer on GPUs, explain how they are used in HPC and AI, and cover some of the specific challenges when running GPU applications on shared clusters. In part II, I'll focus on some of the specific innovations in Univa Grid Engine that help make GPU applications much easier to deploy and manage at scale.
NetApp and Nvidia have introduced a combined AI reference architecture system to rival the Pure Storage-Nvidia AIRI system. It is aimed at deep learning and, unlike FlexPod (Cisco and NetApp's converge infrastructure), has no brand name. Unlike AIRI, neither does it have its own enclosure. A NetApp and Nvidia technical whitepaper – Scalable AI Infrastructure Designing For Real-World Deep Learning Use Cases (PDF) – defines a reference architecture (RA) for a NetApp A800 all-flash storage array and Nvidia DGX-1 GPU server system. There is a slower and less expensive A700 array-based RA.
Seven long months after the next-generation "Volta" graphics architecture debuted in the Tesla V100 for data centers, the Nvidia Titan V finally brings the bleeding-edge tech to PCs in traditional graphics card form. But make no mistake: This golden-clad monster targets data scientists, with a tensor core-laden hardware configuration designed to optimize deep learning tasks. You won't want to buy this $3,000 GPU to play Destiny 2.
Deep learning is a field with intense computational requirements and the choice of your GPU will fundamentally determine your deep learning experience. With no GPU this might look like months of waiting for an experiment to finish, or running an experiment for a day or more only to see that the chosen parameters were off. With a good, solid GPU, one can quickly iterate over deep learning networks, and run experiments in days instead of months, hours instead of days, minutes instead of hours. So making the right choice when it comes to buying a GPU is critical. So how do you select the GPU which is right for you? This blog post will delve into that question and will lend you advice which will help you to make choice that is right for you. TL;DR Having a fast GPU is a very important aspect when one begins to learn deep learning as this allows for rapid gain in practical experience which is key to building the expertise with which you will be able to apply deep learning to new problems.