Little sparse on the documentation side can't tell at a glance if there is a 1:1 hyperperameter tuneability or if this is an opinionated single path locked soft fpga eval-hacking kind of thing.

EDIT: -- Ok, it's legit, here is an example of it put to use by the makers of the Dolphin OpenSource series of FineTunes:

> Here I implement in nano-vllm, efficient sample-K logit extraction, as described in "Sparse Logit Sampling: Accelerating Knowledge Distillation in LLMs" by Anshumann et. al. Sampling occurs on the GPU, the non-sampled logits do not get copied out of GPU space. I tried to implement this in @vllm_project, but it was a bit too heavy for me to figure out.

https://github.com/GeeeekExplorer/nano-vllm/pull/34

This is an incredible achievement for a solo developer. The dev is from the Deepseek team by the way.

After seeing the Docker image for vllm jump +5Gb (to 10Gb!) over the past five months, I grew suspicious of vllm's development practices [1]. It's not easy, for sure, to deal with all those flaky python modules [2].

But having the CUDA packages four times in different layers is questionable! [3]

Yet again, as a college mate of mine used to say, "Don't change it. It works."

--

[1]: https://hub.docker.com/r/vllm/vllm-openai/tags

[2]: https://github.com/vllm-project/vllm/issues/13306

[3]: These kinds of workarounds tend to end up accumulating and never get reviewed back:

- https://github.com/vllm-project/vllm/commit/b07d741661570ef1...

- https://github.com/vllm-project/vllm/commit/68d37809b9b52f4d... (this one in particular probably accounts for +3Gb)

Meta: the Title Casing in the title is pretty obnoxious, "Vllm" is exactly the inverse, casing-wise, of how the project spells its name.

i was skimming through this and kinda surprised how tight the whole thing is. like it does 90% of what vllm does, but the code's readable end to end. no extra infra, no orchestration layers yelling at you. i got it running on local in minutes and throughput actually beat vllm on my 4070. wasn't expecting that.

if we can do this level of performance in 1.2k lines, what if we go the other way split the model across devices or even machines, stream token-by-token, but keep prefix cache consistent across hops. can we design inference engines that think in terms of modular attention scopes instead of monolithic graphs? is it even possible

undefined

Love this project, we need more simplifications like this in the current ML environment

Will this end up getting an open ai compatible web server or is that out of scope.

Did anyone else click in excitedly after misreading ‘Vllm’ as ‘LLVM?’

So... It's a language model..? As in, not "large"? I'm a bit unsure of the magnitudes here, but surely "nano" and "large" cancel out

how did u do it? which portion of vllm refactoring allowed u to get such gains.