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.
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
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."
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.
That is crazy! This is so cool ngl.
Meta: the Title Casing in the title is pretty obnoxious, "Vllm" is exactly the inverse, casing-wise, of how the project spells its name.
Fwiw op has a small window of time to correct the casing after posting
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
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)
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
No, vLLM is a thing for serving language models: https://github.com/vllm-project/vllm
Is it more like llama.cpp then? I don't have access to the good hardware.
llama.cpp is optimized to serve one request at a time.
vllm is optimized to serve many requests at one time.
If you were to fine tune a model and wanted to serve it to many users, you would use vllm, not llama.cpp
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?’
how did u do it? which portion of vllm refactoring allowed u to get such gains.