---
title: Nebius LLMs
 | Developer Documentation
---

This notebook demonstrates how to use LLMs from [Nebius AI Studio](https://studio.nebius.ai/) with LlamaIndex. Nebius AI Studio implements all state-of-the-art LLMs available for commercial use.

First, let’s install LlamaIndex and dependencies of Nebius AI Studio.

```
%pip install llama-index-llms-nebius llama-index
```

Upload your Nebius AI Studio key from system variables below or simply insert it. You can get it by registering for free at [Nebius AI Studio](https://auth.eu.nebius.com/ui/login) and issuing the key at [API Keys section](https://studio.nebius.ai/settings/api-keys).”

```
import os


NEBIUS_API_KEY = os.getenv("NEBIUS_API_KEY")  # NEBIUS_API_KEY = ""
```

```
from llama_index.llms.nebius import NebiusLLM


llm = NebiusLLM(
    api_key=NEBIUS_API_KEY, model="meta-llama/Llama-3.3-70B-Instruct-fast"
)
```

```
None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won't be available and only tokenizers, configuration and file/data utilities can be used.
```

#### Call `complete` with a prompt

```
response = llm.complete("Amsterdam is the capital of ")
print(response)
```

```
The Netherlands! Amsterdam is indeed the capital and largest city of the Netherlands.
```

#### Call `chat` with a list of messages

```
from llama_index.core.llms import ChatMessage


messages = [
    ChatMessage(role="system", content="You are a helpful AI assistant."),
    ChatMessage(
        role="user",
        content="Answer briefly: who is Wall-e?",
    ),
]
response = llm.chat(messages)
print(response)
```

```
assistant: WALL-E is a small waste-collecting robot and the main character in the 2008 Pixar animated film of the same name.
```

### Streaming

#### Using `stream_complete` endpoint

```
response = llm.stream_complete("Amsterdam is the capital of ")
for r in response:
    print(r.delta, end="")
```

```
The Netherlands! Amsterdam is indeed the capital and largest city of the Netherlands.
```

#### Using `stream_chat` with a list of messages

```
from llama_index.core.llms import ChatMessage


messages = [
    ChatMessage(role="system", content="You are a helpful AI assistant."),
    ChatMessage(
        role="user",
        content="Answer briefly: who is Wall-e?",
    ),
]
response = llm.stream_chat(messages)
for r in response:
    print(r.delta, end="")
```

```
WALL-E is a small waste-collecting robot and the main character in the 2008 Pixar animated film of the same name.
```