We ran benchmarks for popular rendering software across all CPU and GPU platforms, across all machine type configurations to determine the performance metrics of each. This article examines the performance of different rendering software on Compute Engine instances. How can you tell if your workload would benefit from a new product available on Google Cloud? You can define each individual resource to complete a task within a certain time, or within a certain budget.īut as new CPU and GPU platforms are introduced or prices change, this calculation can become more complex. With the flexibility of cloud, you can right-size your resources to match your workload. To learn more about comparing on-premises hardware to cloud resources, see the reference article Resource mappings from on-premises hardware to Google Cloud. While this may be a good starting point, the performance of a physical render server is rarely equivalent to a VM running on a public cloud with a similar configuration.

When gauging render performance on the cloud, customers sometimes reproduce their on-premises render worker configurations by building a virtual machine (VM) with the same number of CPU cores, processor frequency, memory, and GPU. To learn more about deploying rendering jobs to Google Cloud, see Building a Hybrid Render Farm. When faced with a looming deadline, these customers can leverage cloud resources to temporarily expand their fleet of render servers to help complete work within a given timeframe, a process known as burst rendering. For our customers who regularly perform rendering workloads such as animation or visual effects studios, there is a fixed amount of time to deliver a project.

Vray render benchmark