This article covers how V-Ray uses your hardware and how to choose the best components for a rendering workstation. The information is structured in a way that helps explain how different hardware components are used by the different computational tasks performed by V-Ray. It will answer questions such as:
- How to choose the best hardware for rendering with V-Ray?
- How V-Ray uses the different hardware components?
Chaos V-Ray — Hardware Recommendations tutorial
Overview
With the rapid advancement of software capabilities and computing power, it is natural to look for a "future-proof" hardware solution that can be used for several years ahead.
Since hardware is a very broad topic with a lot of different aspects, the purpose of this article is to establish the basic guidelines that help to better understand the hardware requirements for rendering with V-Ray.
Note that Chaos' policy is not to give specific recommendations on computer hardware brands, models, configurations, etc.
General
V-Ray is developed and optimized to take advantage of the full capabilities of all hardware components: CPU, GPU, RAM, storage, network and motherboard. As a rule of thumb, the faster the hardware, the faster the rendering.
With V-Ray, both the CPU and the GPU devices are often used simultaneously.
Examples scenarios:
- When using V-Ray CPU, the CPU cores will be used for rendering, while by default, the GPU will handle denoising and lens effects
- When using the V-Ray GPU, the graphics card will be used for rendering, but the Light Cache GI engine may be calculated on the CPU (unless GPU Light Cache is enabled)
- Hybrid rendering within V-Ray GPU, where both the GPU and CPU components render simultaneously
In short, the rendering approach dictates the hardware preference.
V-Ray Benchmark
V-Ray Benchmark is a free tool developed by Chaos specifically designed to test hardware performance with V-Ray. The results page allows you to compare your machine's results to other user-submitted such with various hardware configurations.
Researching such data will give you plenty of information on hardware performance results without the need to physically test configurations. The results include both a CPU and GPU section.
Learn more about V-Ray Benchmark.
Explore the V-Ray Benchmark results.
Note that the CPU results cannot be compared to the GPU results, as the score points are different for the two engines.
Windows, Linux or Mac?
V-Ray is supported on Windows, Linux and macOS systems. The choice comes down to host platform availability for each operating system and personal preference.
As of V-Ray 7, the Metal RT Engine is supported for macOS, for previous versions the CUDA-x86 engine is used.
Learn more in our article about Can I use V-Ray GPU on macOS.
CPU
The CPU is the most crucial hardware component for rendering. It performs almost all of the computations when using the V-Ray CPU engine, which makes it the single unit that influences the render times the most. A faster CPU equals faster rendering.
V-Ray runs on both AMD and Intel processors, so the choice is down to personal preference. Generally, a more powerful CPU is better than a dual-socket or multi-socket CPU system. NUMA configurations also tend to perform worse.
Since the CPU is also utilized for some computations when running V-Ray GPU, generally, a faster CPU will boost GPU render times as well. The CPU can also be used in Hybrid rendering, where it can boost the performance of V-Ray GPU.
GPU
GPU rendering is becoming more popular as hardware gets more affordable, while performance keeps increasing. The faster the graphics card, the faster the V-Ray GPU rendering. Although the GPU devices are not directly used when rendering on V-Ray CPU, they can still be used for speeding up calculations of denoising and lens effects.
V-Ray GPU runs on NVIDIA devices and starting with latest V-Ray 7 releases AMD graphics cards are also supported.
NVIDIA: V-Ray GPU supports CUDA-capable NVIDIA GPUs of the Maxwell generation and later (GeForce 900 series and later). Multiple GPU devices can be stacked together on the same machine to increase performance. Multiprocessor machines can only hold up to a few CPUs, while a dozen or more GPUs can be stacked on the same system.
Mixed architectures cards can also be stacked together as long as the graphic driver supports all of them and the Compute Capability of the cards is 5.2 (Maxwell generation or higher).
AMD: Supported generations are RDNA2, RDNA3, RDNA 3.5 and RDNA4. Generations above RDNA3 are recommended.
For more details take a look at V-Ray GPU minimal and recommended system requirements article.
Memory
Memory has no direct impact on rendering speed. However, the more complex the scene, the more memory it will require.
If the scene requires more memory than the available system memory, then it is very likely that it will not render. The solution is to either install more memory or optimize the scene to use less RAM or VRAM.
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CPU Memory (RAM)
CPU Memory (RAM) is the memory that operates with the processor and is used by the V-Ray CPU render engine. CPU memory is stackable, which means that the total amount of memory is equal to the sum of all memory blocks installed on the system.
NUMA configurations usually have a negative impact on rendering speed. CPUs with uniform memory access should be preferred over non-uniform access (NUMA).
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GPU Memory (VRAM)
VRAM is the memory installed on the GPU, which is different from the system RAM. Unlike the system memory:
- VRAM's pool size is fixed and cannot be extended.
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VRAM cannot be stacked up
Having two GPU devices with 8GB of VRAM each does not equal 16GB of total VRAM.
Since V-Ray GPU needs to copy the scene to every GPU device, the VRAM limit will be the GPU device with the least amount of VRAM. For example, when using two GPUs for rendering, where one has 4GB and the other 8GB of VRAM, you are limited to using 4GB of VRAM.
While VRAM cannot be stacked, it is possible to share the VRAM pool between two GPU devices. This is done by connecting the two NVIDIA GPUs with an NVLink bridge.
While NVink extends the memory pool, there are some limitations:
- NVLink can increase the render time by up to 5% due to data sharing
- V-Ray GPU will not simply copy half of your data to one GPU and the other half to the other. For speed optimizations, some data buffers will still be copied to every GPU;
- You can only connect GPUs of the same model with NVLink. This means that you cannot link an RTX 2080 to an RTX 2070;
- The motherboard needs to support SLI to use NVLink with GeForce cards;
- To use NVLink with Quadro cards, the cards need to be configured to run in TCC (Tesla Compute Cluster) mode, which disables video-out capabilities of the cards;
- Only 1 pair of GPUs connected with NVLink is allowed when using GeForce cards. This limitation does not apply to Quadro cards;
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Cards can be connected with NVLink only in pairs. This means that a setup of 3 or any other odd number of cards limits you to the memory of the card with the lowest amount of VRAM. For best results, use only pairs of NVLink connected cards.
Overclocking
Overclocking can increase hardware performance, but at the same time, it may lead to hardware malfunctioning, affect system stability or introduce random system crashes.
We do not recommend hardware overclocking. If you experience any issues while rendering, we strongly suggest reverting back to the default system clock rates.
Network
Network speed plays an important role when using Network rendering or Distributed rendering or when scenes and assets are stored on a network drive.
When V-Ray starts rendering, it needs to load all the information to the system memory. If the network is too slow and the scene uses a lot of assets, then more time may go into transferring the assets than for actual rendering.
V-Ray does not provide tools for detecting network bottlenecks, but such tools can be found online.
Storage
Just as with networks, storage speed matters when V-Ray reads or writes files. Faster storage drives allow for read and write operations to be performed faster.
Solid-State Drives (SSD) are generally much faster than traditional Hard-Disk Drives (HDD) and allow your applications to start faster, and save files faster. Additionally, the drive can be used to cache scene data.