vLLM can be run on a cloud based GPU machine with [dstack](https://dstack.ai/), an open-source framework for running LLMs on any cloud. This tutorial assumes that you have already configured credentials, gateway, and GPU quotas on your cloud environment.
Next, to provision a VM instance with LLM of your choice (`NousResearch/Llama-2-7b-chat-hf` for this example), create the following `serve.dstack.yml` file for the dstack `Service`:
After the provisioning, you can interact with the model by using the OpenAI SDK:
```python
from openai import OpenAI
client = OpenAI(
base_url="https://gateway.<gatewaydomain>",
api_key="<YOUR-DSTACK-SERVER-ACCESS-TOKEN>"
)
completion = client.chat.completions.create(
model="NousResearch/Llama-2-7b-chat-hf",
messages=[
{
"role": "user",
"content": "Compose a poem that explains the concept of recursion in programming.",
}
]
)
print(completion.choices[0].message.content)
```
```{note}
dstack automatically handles authentication on the gateway using dstack's tokens. Meanwhile, if you don't want to configure a gateway, you can provision dstack `Task` instead of `Service`. The `Task` is for development purpose only. If you want to know more about hands-on materials how to serve vLLM using dstack, check out [this repository](https://github.com/dstackai/dstack-examples/tree/main/deployment/vllm)
