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. More information on V-Ray GPU is available at 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. 2 – CUDA compute capability and card reference 3 – With V-Ray 6, using older drivers (less than 495.xx) on Ampere cards may lead to incorrect render results with RTX. NVIDIA AI Denoiser: Maxwell-, Pascal-, Volta-, Turing-, and Ampere-based NVIDIA card with latest recommended video driver 3ġ – Windows 11 is required for running Intel Alder Lake processors.

V-Ray Production Denoiser: AMD or NVIDIA GPU supporting OpenCL 1.2 NVIDIA RTX with compute capability 5.2 2: RTX cards with latest recommended video driver 3 NVIDIA CUDA with minimum required compute capability 5.2 2: Maxwell-, Pascal-, Volta-, Turing-, and Ampere- based NVIDIA card (s) Only IPv4 required for distributed rendering is supported. Required for customers using legacy hardware lock, preferably USB 2.0 Intel 64 1, AMD64 or compatible processor with SSE4.2 support

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