Redshift is efficient when it comes to VRAM utilization. The text below explains how Redshift uses VRAM so that users can make an informed decision when choosing a GPU. However, GPUs with more VRAM are also more expensive. The general rule of thumb for Redshift (and other GPU renderers) is “the more VRAM, the better”. GPUs come in multiple VRAM configurations like 8GB/11GB/12GB/16GB/24GB/48GB. video RAM) is enough and what difference does it make to performance? Do you need more VRAM? If so, Titan/Quadro/Tesla (for NVidia) or Radeon Pro (for AMD) is the right choice for you If you don’t need either of the above, multiple cheaper GPUs (for the same cost) will offer more compute power and faster render times.Having multiple GPUs might require a special motherboard/CPU/setup which is outlined later in this document. If you install multiple GPUs on the same computer, Redshift will render faster. The performance of the non-duo is roughly equivalent to the W5700X which is another GPU we’d recommend for Redshift, as it’s considerably cheaper. The non-duo version contains a single GPU so it’s half as fast. The best AMD GPU rendering performance is currently offered by the Radeon Pro Vega II duo as this graphics board contains two GPU chips. Support for Windows and Linux might arrive in a future version of Redshift. As mentioned on that page, AMD GPUs are currently only supported on macOS Big Sur or later. Please read this for more information on how to enable it: AMD (only for macOS Big Sur or later)įor a list of supported AMD GPUs, please see the bottom of this page. This was first introduced with a Windows 10 update and the latest NVidia drivers sometime in 2020. In other words, the CPU-GPU communication on Linux is, by default, faster than on Windows (with WDDM) across all NVidia GPUs, including GeForce and Quadro/Tesla/Titan GPUs.ĭespite the lack of TCC on GeForce GPUs, you can still get some of the latency benefits of TCC on Windows 10 by enabling “Hardware-accelerated GPU scheduling”. THe Linux operating system does not need it because the Linux display driver doesn’t suffer from latencies typically associated with WDDM. As mentioned above, TCC is only useful for Windows. Only Quadros, Teslas and Titan GPUs can enable TCC. It becomes exclusive to CUDA applications, like Redshift. The drawback of TCC is that, once you enable it, the GPU becomes ‘invisible’ to Windows and 3d apps (such as Maya, Houdini, etc). It bypasses the Windows Display Driver Model (WDDM) and allows the GPU to communicate with the CPU at greater speeds. It is a special driver developed by NVidia for Windows. One important difference between GeForce GPUs and Titan/Quadro/Tesla GPUs is TCC driver availability. With Redshift, it’s possible to mix GeForce and Quadro GPUs on the same computer. The one key benefit Quadros have over GeForces is that they often have more onboard VRAM which might be important for you if you are rendering very large scenes. The Quadros can typically render viewport OpenGL faster compared to the GeForces but that doesn't affect Redshift’s rendering performance. Please note that there are no considerable performance differences between GeForces and Quadros as far as Redshift rendering is concerned. From the professional-grade GPUs, we recommend the last-gen Quadro RTX5000, Quadro RTX6000 GPUs or the next-gen Quadro RTX A6000. Or the current-gen, we recommend the GeForce RTX3060 Ti, RTX3070, GeForce RTX3080 or GeForce RTX3090 GPUs. NVidia (for Windows, Linux or macOS High Sierra)įrom the NVidia line of GPUs, we recommend the last-gen GeForce RTX2070, GeForce RTX2070Ti, GeForce RTX2080 and GeForce RTX2080Ti GPUs. It also supports AMD GPUs on macOS BigSur or later. Redshift currently supports NVidia GPUs on Windows, macOS (up to High Sierra) and Linux. If the CPU will be driving four or more GPUs or batch-rendering multiple frames at once, a higher-performance CPU such as the Intel Core i7 is recommended. While Redshift doesn't need the latest and greatest CPU, we recommend using at least a mid-range quad-core CPU such as the Intel Core i5. Depending on scene complexity, these processing stages can take a considerable amount of time and, therefore, a lower-end CPU can 'bottleneck' the overall rendering performance. These include extracting mesh data from your 3d app, loading textures from disk and preparing the scene data for use by the GPU. There are, however, certain processing stages that happen during rendering which are dependent on the performance of the CPU, disk or network. Since Redshift is a GPU renderer, it mostly depends on GPU performance.
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