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theanonymousone
KVarN: Native vLLM backend for KV-cache quantization by Huawei github.com

throwa356262an hour ago

Better performance than TQ and better quality than FP16?

Am I reading this right??

qeternity16 minutes ago

It's not better quality: 59.3% vs 59.4% fp16 on AIME 25

thefox9618 minutes ago

Faster than Fp16, not better quality i guess

pbich25 minutes ago

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v3ss0nan hour ago

Why this is not a PR for vLLM ?

esafakan hour ago

It's the output of a research paper; the authors are not trying to build up vLLM, and they probably have no incentive to do so. You can submit a PR, though! It's easier now while the divergence is low, so don't wait. Since there are six authors, I bet you could get help with the inevitable review chores if you just take the step of creating the PR.

edit: It might not be clear that it is based on vLLM 0.22, which is the current version: https://github.com/huawei-csl/KVarN/commit/d6290e99098d7426d.... All you have to do is create a diff off it; it's fairly straightforward.

jmalickian hour ago

And with the help of AI, pointing at AI at this paper and saying "making a vLLM PR from this paper" tends to work surprisingly well, even if you need to nudge it a little bit along the way.

shockembopper2 minutes ago

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