Global Volumes aren't "network volumes, but better." Here's what they're for.

Global Volumes use object storage to make large data available across Runpod datacenters, while network volumes behave like conventional remote filesystems tied to a specific location. Although both appear as mounted folders, Global Volumes are best suited to large, relatively static files such as model weights, datasets, training checkpoints, generated outputs, and archives because object storage efficiently handles whole-file reads and writes. They are poorly suited to running applications such as ComfyUI, Python environments, Git repositories, databases, logs, and caches, which depend on frequent small-file operations, file locking, symlinks, permissions, and in-place changes. The recommended approach is to run code and application installations from local or network storage near the GPU while keeping models and other portable data on Global Volumes, allowing workloads to use available GPUs in any datacenter without repeatedly transferring large files.

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