[Bug]: The openai deployment model takes twice as long to deploy as fastapi's approach to offline inference.

[LIUKAI0815]

Your current environment

vllm=0.4.2

🐛 Describe the bug

Same input and output, openai is slow

openai:  python -m vllm.entrypoints.openai.api_server  

fastapi:
from vllm import LLM, SamplingParams

# Sample prompts.
prompts = [
    "Hello, my name is",
    "The president of the United States is",
    "The capital of France is",
    "The future of AI is",
]
# Create a sampling params object.
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

# Create an LLM.
llm = LLM(model="facebook/opt-125m")
# Generate texts from the prompts. The output is a list of RequestOutput objects
# that contain the prompt, generated text, and other information.
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
    prompt = output.prompt
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

[DarkLight1337]

To better understand the issue, please show how you timed the code. You might have included the startup time of the offline inference LLM which would make it an unfair comparison.

[LIUKAI0815]

I made the wrong comparison