throwa356262 1 day ago

Better performance than TQ and better quality than FP16?

Am I reading this right??

  • thefox96 1 day ago

    Faster than Fp16, not better quality i guess

  • qeternity 1 day ago

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

    • sheepscreek 17 hours ago

      0.1% is within margin of error. Depending on the performance boost, it might be worthwhile taking a minuscule quality hit.

  • electroglyph 20 hours ago

    any divergence (even if the benchmark is better) from full precision is error

    • 7e 13 hours ago

      Just pretend that it is the next step update when training. You didn’t train your model to step=inf, I hope?

v3ss0n 1 day ago

Why this is not a PR for vLLM ?

  • esafak 1 day 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.

    • jmalicki 1 day 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.

  • thefox96 1 day ago

    it should be easy to do btw

  • woadwarrior01 22 hours ago

    Last I heard, vLLM was backed by a company that has raised $150m in seed funding. I'm sure they've got the resources to port it.

  • electronsoup 19 hours ago

    Why this is not a PR for llama.cpp

0xjeffro 20 hours ago

yao yao ling xian