Визуализация на GPU¶

GPU rendering makes it possible to use your graphics card for rendering, instead of the CPU. This can speed up rendering because modern GPUs are designed to do quite a lot of number crunching. On the other hand, they also have some limitations in rendering complex scenes, due to more limited memory, and issues with interactivity when using the same graphics card for display and rendering.

To enable GPU rendering, go into the Preferences ‣ System ‣ Cycles Render Devices, and select either CUDA, Optix or OpenCL. Next, you must configure each scene to use GPU rendering in Properties ‣ Render ‣ Device.

Supported Hardware¶

Blender supports two different technologies to render on the GPU depending on the particular GPU manufacture.

Nvidia¶

CUDA and Optix are supported for GPU rendering with Nvidia graphics cards.

CUDA¶

CUDA requires graphics cards with compute capability 3.0 and higher. To make sure your GPU is supported, see the list of Nvidia graphics cards with the compute capabilities and supported graphics cards. CUDA GPU rendering is supported on Windows, macOS, and Linux.

Optix¶

For RTX graphics cards with hardware ray tracing support (e.g. Turing), Optix can be used for better performance. Optix support is still experimental and does not yet support all features, see below for details.

Optix requires Geforce or Quadro RTX graphics card with recent Nvidia drivers, and is supported on Windows and Linux.

AMD¶

OpenCL is supported for GPU rendering with AMD graphics cards. Blender supports graphics cards with GCN generation 2 and above. To make sure your GPU is supported, see the list of GCN generations with the GCN generation and supported graphics cards.

AMD OpenCL GPU rendering is supported on Windows and Linux, but not on macOS.

Поддерживаемые возможности и ограничения¶

CUDA and OpenCL rendering supports all the same features as CPU rendering, except two:

Open Shading Language.
Advanced volume light sampling to reduce noise.

Optix support is experimental and does not yet support the following features:

Baking
Branched Path Tracing
Ambient Occlusion and Bevel shader nodes
Combined CPU + GPU rendering
Using CPU memory for bigger scenes

Часто задаваемые вопросы¶

Почему Blender перестаёт отвечать во время визуализации?¶

Когда графическая карта занята визуализацией, она не может перерисовывать пользовательский интерфейс, из-за чего Blender перестаёт отвечать. Мы пытаемся обойти эту проблему, забирая контроль над GPU как можно чаще, но гарантировать полностью гладкую работу мы не можем, особенно на тяжёлых сценах. Это ограничение графических карт и для него не существует стопроцентно работающего решения, хотя мы и постараемся в будущем улучшить этот момент.

Если у вас есть возможность, лучше установить более одного GPU и использовать один из них для отображения, а остальные задействовать для визуализации.

Почему сцена, которая визуализируется на центральном процессоре, не визуализируется на видеокарте?¶

There maybe be multiple causes, but the most common is that there is not enough memory on your graphics card. Typically while using GPU rendering the GPU can only use the amount of memory that is on the GPU. This is usually much smaller then the amount of system memory that the CPU uses. In the case that the GPU runs out of memory Blender will automatically try to also use system and GPU memory. This has a performance impact but it is still faster than using CPU rendering. This feature does not work on OpenCL rendering.

Можно ли для визуализации использовать несколько видеокарт?¶

Yes, go to Preferences ‣ System ‣ Compute Device Panel, and configure it as you desire.

Могут ли несколько видеокарт увеличить доступную память?¶

Нет, каждая видеокарта имеет доступ только к своей собственной памяти.

What renders faster, Nvidia or AMD, CUDA or OpenCL?¶

Currently Nvidia with CUDA is rendering fastest, but this really depends on the hardware you buy. Currently, CUDA and OpenCL are about the same in the newest mid-range GPUs. However, CUDA is fastest in the respect of high-end GPUs.

Сообщения об ошибках¶

In case of problems, be sure to install the official graphics drivers from the Nvidia or AMD website, or through the package manager on Linux.

Unsupported GNU version! gcc 4.7 and up are not supported! (Неподдерживаемая версия GNU! gcc 4.7 и старше не поддерживаются!)¶

On Linux, depending on your GCC version you might get this error. There are two possible solutions:

Use an alternate compiler

If you have an older GCC installed that is compatible with the installed CUDA toolkit version, then you can use it instead of the default compiler. This is done by setting the CYCLES_CUDA_EXTRA_CFLAGS environment variable when starting Blender.

Launch Blender from the command line as follows:

CYCLES_CUDA_EXTRA_CFLAGS="-ccbin gcc-x.x" blender

(Substitute the name or path of the compatible GCC compiler).

Remove compatibility checks

If the above is unsuccessful, delete the following line in /usr/local/cuda/include/host_config.h

#error -- unsupported GNU version! gcc 4.7 and up are not supported!

This will allow Cycles to successfully compile the CUDA rendering kernel the first time it attempts to use your GPU for rendering. Once the kernel is built successfully, you can launch Blender as you normally would and the CUDA kernel will still be used for rendering.

CUDA Error: Invalid kernel image (Ошибка CUDA: Неверное ядро изображения)¶

If you get this error on Windows 64-bit, be sure to use the 64-bit build of Blender, not the 32-bit version.

CUDA Error: Kernel compilation failed (Ошибка CUDA: Сбой компиляции ядра)¶

This error may happen if you have a new Nvidia graphics card that is not yet supported by the Blender version and CUDA toolkit you have installed. In this case Blender may try to dynamically build a kernel for your graphics card and fail.

В таком случае вы можете:

Check if the latest Blender version (official or experimental builds) supports your graphics card.
Если вы сами собирали Blender, попробуйте скачать и установить новейший набор инструментов для разработчика CUDA.

Обычным пользователям не требуется устанавливать набор инструментов CUDA, поскольку Blender уже поставляется со скомпилированными ядрами.

CUDA Error: Out of memory (Ошибка CUDA: Не хватает памяти)¶

Обычно эта ошибка означает, что для хранения сцены на видеокарте не хватает памяти. На текущий момент мы можем визуализовывать только те сцены, которые влезают в память видеокарты, которая обычно меньше памяти, доступной центральному процессору. Подробности смотрите выше.

Примечание

One way to reduce memory usage is by using smaller resolutions for textures. For example, 8k, 4k, 2k, and 1k image textures take up respectively 256MB, 64MB, 16MB and 4MB of memory.

The Nvidia OpenGL driver lost connection with the display driver¶

If a GPU is used for both display and rendering, Windows has a limit on the time the GPU can do render computations. If you have a particularly heavy scene, Cycles can take up too much GPU time. Reducing Tile Size in the Performance panel may alleviate the issue, but the only real solution is to use separate graphics cards for display and rendering.

Another solution can be to increase the time-out, although this will make the user interface less responsive when rendering heavy scenes. Learn More Here.

CUDA error: Unknown error in cuCtxSynchronize() (Ошибка CUDA: Неизвестная ошибка в cuCtxSynchronize())¶

An unknown error can have many causes, but one possibility is that it is a time-out. See the above answer for solutions.