Benchmark Results

These benchmarks were run using Iray RTX 2019.1.1. Cards represented are of the Maxwell, Pascal, Volta and Turing 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 EC2, Microsoft Azure, Google Compute Engine, Linode and Scaleway. Please contact us if you are a provider and want to be listed.

A Note on RTX Performance

The performance of hardware which supports RT Core hardware ray-tracing such as GeForce RTX, Quadro RTX and the Tesla T4 cards can be complex to benchmark due to a high degree of scene dependence. We have written a dedicated article to explain RTX performance here.

RTX Performance Explained

What this means is that while these benchmark results are accurate for our tested scene, you may see better or worse performance if your scene differs significantly. Keep this in mind when evaluating Turing based hardware.

Bare Metal Performance

These results are for Iray Photoreal mode. In general we have found relative performance between GPU hardware is very similar for Iray Interactive mode.

 

Megapaths/sec

 

Cloud Provider Performance

Cloud Providers

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.

All

 

Megapaths/sec

 

Nimbix

 

Megapaths/sec

 

AWS

 

Megapaths/sec

 

Azure

 

Megapaths/sec

 

Google

 

Megapaths/sec

 

Linode

 

Megapaths/sec

 

Scaleway

 

Megapaths/sec

 

Notes

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.

Multi-GPU Scaling Efficiency

Iray Photoreal mode offers extremely good scaling efficency across multiple GPUs. Many rendering solutions see significantly diminishing returns as more GPUs are added. In contrast Iray continues to deliver scaling efficiency over 90% even with eight GPUs in a single system. Here are some results (efficiency in brackets).

 

Megapaths/sec

 

In our experience the type of GPU does not significantly affect the scaling efficiency. We have even seen one user utilise a machine with 16 GPUs installed and they still saw over 90% efficiency.

Testing Methodology

All benchmarks have been performed under Linux (usually CentOS 7.5) with the latest available NVIDIA drivers (as of writing 430.26). 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 below is a fully converged version of our test scene.

Iray Benchmark Scene
Iray Benchmark Scene – Model by Evermotion

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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