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Your intuition is correct and the sibling comments are wrong. Modern LLM inference servers support hierarchical caches (where data moves to slower storage tiers), often with pluggable backends. A popular open-source backend for the "slow" tier is Mooncake: https://github.com/kvcache-ai/Mooncake

OK that's pretty fascinating, turns out Mooncake includes a trick that can populate GPU VRAM directly from NVMe SSD without it having to go through the host's regular CPU and RAM first!

https://github.com/kvcache-ai/Mooncake/blob/main/doc/en/tran...

> Transfer Engine also leverages the NVMeof protocol to support direct data transfer from files on NVMe to DRAM/VRAM via PCIe, without going through the CPU and achieving zero-copy.

They are not caching to save network bandwidth. They are caching to increase interference speed and reduce (their own) costs.

That is slow.