~50% GPU utilization of openai API server

[imoneoi]

I run an openai API server with a LLaMA-based model and 128 parallel requests, but only about 50% GPU utilization (nvidia-smi). Is it normal? Or because of some overhead, such as tokenizer?

[WoosukKwon]

Hi @imoneoi, thanks for the question. Yes, vLLM has some CPU-side overheads that can potentially decrease its GPU utilization. For example, the tokenizer can be a performance bottleneck especially when the slow tokenizer is used. For another example, the sampler part can be slow, especially when the requests have different sampling parameters (e.g., some requests use nucleus sampling and others use beam search). Also, FastAPI may incur some overhead when the request rate is high. For now, it's difficult to tell which one is causing the slowdown.

Thanks again for reporting it. We will continue to identify and optimize the performance issue.

[saareliad]

@WoosukKwon @imoneoi Note that nvidia smi only measures the time when a kernel is running, not the true utilization. One can write a "wait kernel" which would get 100% utilization in nvidia smi ;) So better measure FLOPS/THEORETIC_FLOPS or alike. ref https://stackoverflow.com/questions/40937894/nvidia-smi-volatile-gpu-utilization-explanation.,

@WoosukKwon do you think that these overheads you mentioned are really major? @imoneoi Can you recall some CPU usage statistics during the run? I guess I'll try viewing these statistics myself soon on vLLM (I'm intentionally looking for bottlenecks to solve)

[supersteves]

I have a similar issue.

I'm using VLLM with Qwen2.5 32b GGUF q4 via the OpenAI endpoints to do chat completion. I know this is experimental, but am VRAM poor and need this kind of model. (OK, I'm testing in the cloud with short-lived machines, but am looking for something that can be used beyond testing without ridiculous cost).

I am playing with increasing the number of GPU expecting to halve the token generation time each time I double the GPUs, approximately.

I'm finding that the GPUs hit around 80-95% usage (which is good) but after a certain number of GPUs (depending on the GPU model), it hits a wall in terms of performance gain.

top shows CPU is almost exactly 100% (i.e. a single core).

And the worst thing about it is that it doesn't matter if I have more than 2 vCPUs, it's always hitting the 1 (real) core 100% usage bottleneck. I've tried with 24 (12 real) cores, same.

This is a single chat request, not parallel calls. I've played with all the options I can find related to concurrency, parallel, threads.

I guess it's a slow tokeniser, but also one that isn't able to use multithreading?

[tempcollab]

also facing the same issue with llama 3.1 models on 2 x H100 SXM

[ChiNoel-osu]

I too have a similar issue with OpenAI API server.
Rocking a Xeon CPU + RTX A6000
The process only uses single core (100~101 usage in top). While it's fine for one or two requests at a time, when it hits 3 and above, the GPU utilization becomes fairly low and token per second & TTFT also have very noticeable decrease. It can even cause timeout issues on OpenAI client if they have short timeout times.

Whatever is eating up the CPU, I would hope it can spread across available CPU threads.

[ChiNoel-osu]

Turns out guided decoding backend lm-format-enforcer uses a lot of CPU. Was able to fix low TPS by changing it to xgrammar as outlines doesn't work.
But still, it using single CPU core could be a bottleneck in the future.