4 ms·

"pay for data on VRAM" RAM of GPU

• criemen 1y ago
  But that doesn't make sense? Why would they keep the cache persistent in the VRAM of the GPU nodes, which are needed for model weights? Shouldn't they be able to swap in/out the kvcache of your prompt when you actually use it?
  • minimaxir 1y ago
    That is slow.
  • dotancohen 1y ago
    They are not caching to save network bandwidth. They are caching to increase interference speed and reduce (their own) costs.
  • tazjin 1y ago
    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 https://github.com/kvcache-ai/Mooncake
    • simonw 1y ago
      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/transfer-engine.md#2-nvmeof-segment 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.