Managing Multi-GPU Workloads with Vulkan Device Groups
Learn how to use Vulkan Device Groups to treat multiple GPUs as a single logical device, simplifying multi-GPU rendering and resource replication.
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vulkanChoosing Between PyTorch DataLoader and DistributedSampler for Multi‑GPU Training
Decide when to use a plain DataLoader versus pairing it with DistributedSampler for efficient, non‑overlapping data access across multiple GPUs.
pytorchCycles OptiX multi‑GPU device limit configuration
The goal is to enable Cycles rendering with the OptiX backend on three or more identical NVIDIA RTX GPUs so that memory and compute resources scale across all devices. Official documentation does not state a maximum number of GPUs that can be used simultaneously with OptiX, and user reports suggest that enabling more than two GPUs may cause Cycles to fall ba
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