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vLLM is an open-source library designed for efficient and high-throughput serving of large language models (LLMs). It leverages PagedAttention, an optimized memory management technique, to enable faster inference and better memory utilization, making it ideal for serving popular models like LLaMA, Falcon, and GPT variants. vLLM supports both single-GPU and multi-GPU setups, allowing scalable deployment of LLMs with lower latency and higher token generation speed. It is widely used in AI applications for tasks such as text generation, summarization, and chat-based interactions.

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Get started

Go to the apps section in the web console and click either the small, medium or large instance of vLLM. This will give you some good default settings, but you can fully customise your deployment at the next step. Note: You will need to enter your HuggingFace model id and your HuggingFace API token. Please be aware that many of the models are gated, you will need to go to the model page and sign an agreement and wait for approval before you can use the model. If you try to use a model without having approval vLLM won’t work.

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Model selection

The supported model list is here: vLLM models They include these categories:
  • Text Generation
  • Text Embedding
  • Reward Modeling
  • Classification
  • Sentence Pair Scoring
  • Multimodel Text, Image, Video, Audio
  • Transcription

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GPU selection

The model(s) you wish to run will determine the amount of VRAM you will need on your GPU. There is a calculator here: LLM-Model-VRAM-Calculator

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Using vLLM

When you deploy the VM you will be shown the VM information page. On the left hand side there is a pane called ‘Metadata’. For vLLM we can see the following metadata: 
CUDO_HF_TOKEN	hf_xxxxxxx
CUDO_MODEL	mistralai/Mistral-7B-v0.1
CUDO_TOKEN	cudo_gjusyaoesy
appId	vllm
port	8000
  • CUDO_HF_TOKEN the HuggingFace token you provided
  • CUDO_MODEL the HuggingFace model you provided
  • CUDO_TOKEN the token generated to act as your API key / password
  • port the port to connect to your vLLM instance on
Additionally, you will need your VM IP address from the VM information page.

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Open AI API

Now the model is ready, you can use openai python library: 
pip install openai
Make sure to replace CUDO_TOKEN and VM-IP-ADDRESS with the data from the previous step. 
from openai import OpenAI

client = OpenAI(
    api_key="CUDO_TOKEN",
    base_url="http://VM-IP-ADDRESS:8000/v1",
)

models = client.models.list()
print(f"Available models: {models.data}")
model = models.data[0].id

# Completion API
stream = False
completion = client.completions.create(
    model=model,
    prompt="A robot may not injure a human being",
    echo=False,
    n=2,
    stream=stream,
    logprobs=3)

print("Completion results:")
if stream:
    for c in completion:
        print(c)
    for c in completion:
        if c.choices:
            print(c.choices[0].text)
else:
    print("Full completion:")
    print(completion)
    for i, choice in enumerate(completion.choices, start=1):
        print(f"Choice {i}:")
        print(choice.text.strip())
See more here: OpenAI docs :LearnMore