Inference is exceptionally slow on the L20 GPU

[joey9503]

speed is 0.08tokens/sec and the gpu usage is extremely low:

system info:
gpu: L20
cuda: 12.2
pytorch: 2.5.1
graphics card driver version: 535.161.08
vllm version: 0.6.4.post1

inference script:

from transformers import AutoTokenizer
from vllm import LLM, SamplingParams

# Initialize the tokenizer
tokenizer = AutoTokenizer.from_pretrained("../Qwen2-Math-7B-Instruct")

# Pass the default decoding hyperparameters of Qwen2.5-7B-Instruct
# max_tokens is for the maximum length for generation.
sampling_params = SamplingParams(temperature=0.7, top_p=0.8, repetition_penalty=1.05, max_tokens=512)

# Input the model name or path. Can be GPTQ or AWQ models.
llm = LLM(model="../Qwen2-Math-7B-Instruct", enforce_eager=True)

# Prepare your prompts
prompt = "Tell me something about large language models."
messages = [
    {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

# generate outputs
outputs = llm.generate([text], 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}")

[qiuxin2012]

Thanks for your question. We don't support nvidia GPUs.