Speculative KV coding: losslessly compressing KV cache by up to ~4×

TL;DR (and please correct me if I got it wrong):

Tiny deterministic model predicts the K/V cache, prediction is compared with reality, delta is stored in vram. The other way round then just predicts the values again, applies the delta, and you have the full correct value while just storing the delta

And this works because you're never looking at the whole k/v cache but always just a slice. So you just need a memory buffer of the size of the slice

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If this works out and I've understood correctly, that _I think_ would mean that a 24GB RTX 4090 could fit 256k q8 context next to Qwen3.6-27B at IQ4_NL.

Or, alternatively, something like 208k context (matching claude api limits of 200k in some plans) with a slightly larger quant like UD-Q4_K_XL.

That would be massive. Especially since the thing has so much compute to spare.

Though, all depending on the size of that predictor model I guess?

If “speculative” approach works so well in different contexts why not make it first class and use everywhere, possibly recursively?

The problem with this approach is that even recomputing a "draft" of the KV cache is still quadratic in context length. Maybe you can get some constant savings by always recomputing the earliest tokens, but it's not a good tradeoff as context sizes grow.

Sure, but any classical attention mechanism is quadratic in context length.

The tradeoff gets better the bigger your primary model, and probably with bigger batch sizes. The KV cache can consume a lot of expensive VRAM, and the VRAM and compute costs of the predictor model become a small fraction of the cost of the primary model

For serving a 1T model with 16 concurrent requests this could make a lot of sense. For a 8B model with a single request far less so