“At BOXX, we also understand that time is critical. “Datalogue delivers ROI for organizations by empowering them to make the right decisions faster,” Bill Leasure, BOXX VP of Marketing. Held through March 21 in San Jose, GTC is the premier AI and deep learning event, providing attendees with training, insights, and direct access to experts from NVIDIA and other leading organizations. Since Tensor Core compatibility has improved drastically since the last time we updated this post, we now believe that Tensor Cores will greatly impact actual performance for many users, so it is now included in the final calculation.AUSTIN, TEXAS and SAN JOSE, CA, March 19, 2018-BOXX Technologies, the leading innovator of high-performance computer workstations, rendering systems, and servers, today announced that the APEXX W3 data science workstation will demonstrate Datalogue’s proprietary software designed to reconcile disparate data sets at BOXX booth #520 at NVIDIA’s GPU Technology Conference (GTC). Note, in previous versions of this blog post, we did not use the Tensor Core metric in the performance score. For cards that are not sold by vendors anymore, we listed the approximate second-hand price. The market value of a GPU changes all the time, so some of them might be out of date. We used estimates of the actual market price to calculate "bang for buck" by looking at the sales prices of major retailers in Europe. If you are on a budget, you can often get a last-gen card second-hand for a much lower price. The flagship GPU of a specific generation is not necessarily the best value. Note that the pricing does not increase linearly with performance. Unless you intend to use your GPU in a data center, consumer cards usually provide the best value. In terms of pricing: the RTX 3080 goes for around €1000,00 in Europe, compared to €2500,00 for the RTX A5000 and €6000,00 for the A10. The TDP of the 3080 is 320 watts, compared to 230 for the A5000 and just 150 for the A10. Regarding VRAM, the RTX 3080 has 10GB, and the other two have 24GB. All three cards perform similarly in the number of floating point operations they can do per second. To illustrate the difference, let's take a look at three GPUs with very similar performance characteristics: the consumer RTX 3080, the workstation RTX A5000 and the server NVIDIA A10 PCIe. NVIDIA specifically forbids the use of consumer-grade cards for this use case.įinally, the significant difference between these three categories is price. It is also essential to understand that NVIDIA's workstation and server products are licensed for use in "data center" deployments. Both server and workstation GPUs often come with more VRAM than their consumer-grade counterparts, matching what is often required by professionals. Workstation-grade GPUs are smaller (and less cool-looking) than their consumer-grade equivalents, and their power draw is smaller. NVIDIA targets 3D modelers, game developers, AI developers, and anyone requiring GPUs to do their job. Workstation-grade cards are mostly meant to be used in professional industry. Second, their power draw is as low as possible since power usage directly affects the bottom line when deploying at scale. They are usually optimized to be small, so many can fit in a few servers. Server-grade cards are meant for use in data centers and high-performance computing clusters. In most cases, the actual chips used in these cards are the same but artificially limited to achieve product diversity across NVIDIA's product line-up. NVIDIA's products are categorized into roughly three categories: consumer-grade, workstation-grade and server-grade. Most of these features are also present in the consumer line-up of GPUs. Apart from Tensor Cores, NVIDIA is continually updating its hardware platform as well as its software to provide new features and performance improvements for deep learning. These cores are specifically meant to supercharge AI workloads. Ever since the Volta generation, their GPUs come equipped with Tensor Cores. NVIDIA recognizes that gamers are no longer the only audience for their products. This blog post is here to help you make an informed decision when selecting the ideal GPU for your deep learning projects! With supply chain problems gradually being resolved and prices becoming more stable, many individuals are keen to get their hands on the latest NVIDIA GPUs. Even though graphics processors were initially intended for gaming, computer science enthusiasts are well aware that they hold significant value in numerous other areas.
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