Iray Performance Benchmarks and Hardware Setup

Benchmark Results

These benchmarks were run using Iray 2017.1.2. Cards represented are of the Kepler, Maxwell, Pascal and Volta architectures. For cards with the same architecture which we have not listed you can usually extrapolate a reasonable estimate from the specifications.

We also include results for cloud offerings as well, including Nimbix, Amazon EC2Microsoft Azure and Google Compute Engine. Please contact us if you are a provider and want to be listed.

Bare Metal Performance

Iray Photoreal

These results are for Iray Photoreal. Note that for RTX, Iray 2017.1.2 does not utilise the new RT Core raytracing technology. As such the performance data for RTX cards is purely for CUDA core usage. For more details see the testing methodology below the results.





Cloud Provider Performance

An increasing number of cloud providers are offering on-demand GPU resources. This is great news for users of Iray enabled applications and to give you an idea of what performance to expect relative to bare metal hardware you might find in your own computers we have run all of the tests for you. These tests are all GPU only, in some cases you may obtain additional performance by enabling the CPU as well but in our experience those resources are better left for other tasks.

Iray Photoreal





























For Google Compute Engine, rather than a pre-configured machine type consisting of a CPU and GPU, you attach GPU types to any of the supported machine types. This means you can actually run larger numbers of GPUs on machines with much less CPU resource than normal. Note however that you must have at least as many CPU cores as you have physical GPU chips to get full use out of all of the GPUs.

DGX-1 and Quadro VCA

The DGX-1 and Quadro VCA are an appliance offering which has been specifically tuned to run demanding GPU accelerated applications. In addition to very high performance, the Quadro VCA specifically offers Iray IQ mode allowing large numbers of Quadro VCA appliances to be interconnected for extreme performance. We didn’t have access to large quantities of Quadro VCA appliances for our tests, however below are the results for a single Quadro VCA to give you a feeling for the performance as well as a single DGX-1. We put this in its own section because including it in the main bare metal graph scales the single cards too small!

Iray Photoreal




Testing Methodology

All benchmarks have been performed under Linux (usually CentOS 7.4) with the latest available NVIDIA drivers (as of writing 384.98). Some tests were performed on older drivers where administrative access to the machines was not available. Where we have full control over the environment the following setup was utilised.

Configuration Item Value
Operating System CentOS Linux 7.4.1708
Linux Kernel Version 3.10.0-514.10.2
NVIDIA Driver Version 384.98
ECC Mode Off
CPU Disabled
Iray Version Iray 2017.1.2 build 296300.3713
CPU Intel Core i5 6500
Memory 16GB DRR4-2133
Chipset Intel H170
PCIe v3.0 Full 16 Lanes
Image Resolution 3840×2160
Iteration Count 250

Iray Benchmark Scene - Model by Evermotion

In order to ensure we are testing raw Iray performance we have developed a stand-alone benchmark tool based on Iray. Our tool renders a fixed scene multiple times and averages the results to ensure consistency. To ensure the results mean something for real-world use we utilise a non-trivial test scene, ensuring the GPUs have plenty of work to do. The image above is a fully converged version of our test scene.

Note that these benchmarks are not performed in a way that they can be compared to the previous series of benchmarks migenius conducted which is why we are retesting even the older cards where possible. This is due to changes in Iray itself, new Iray versions often change the relationship between iteration count and quality which can affect our absolute measurements. However all relative measurements between cards within the benchmark are valid.

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