That's because DeepSeek uses MLA which apparently does allow offloading the KV cache. That doesn't apply to all models, particularly the open-weight models that are primarily GQA AFAIK.
Any models allow offloading KV cache, it’s not a matter of model architecture but only of the implementation. The only somewhat difference can be for non transformer models. But for all attention models it’s the same – blob of data per each token. It’s much worse for older models with MHA because their KV cache is just too big, and it’s better for DeepSeek because their KV cache is the smallest. But it’s alright for current generation of GQA models as well.
Are you sure about that? GQA applies self-attention to every KV cache entry. If you're offloading, then you're having to dynamically page in all the KV cache entries into the GPU which is quite slow since the CPU/GPU link only has so much bandwidth. My understanding is that MLA reduces the size of the KV cache & doesn't necessarily attend to every KV token at every step which is why offloading to disk works (i.e. most of the tokens can remain on disk without ever being loaded into the GPU).
Offloading in this case doesn’t mean keeping the kv cache on the disk/in storage all the time, it means keeping it there when request isn’t in process of generation. While request being generated kv cache is indeed in vram.
As for MLA - Deepseek is, just like others, attend to all historical tokens. The only difference instead of having actual KV entries it has lower dimension KV entries, which are being projected into full blown KV entries on the fly during attention. It’s similar to GQA, just instead of just duplication KV entries by size of groups it applies linear transformation.
Ah OK. So this is for resuming chat context cheaply. What I said is still correct - 3FS is not part of the inference flow & not relevant to the paper which is about optimizing the KV cache usage at runtime.
