ollama37/docs/cloud.mdx

---
title: Cloud
sidebarTitle: Cloud
---

<Info>Ollama's cloud is currently in preview.</Info>

## Cloud Models

Ollama's cloud models are a new kind of model in Ollama that can run without a powerful GPU. Instead, cloud models are automatically offloaded to Ollama's cloud service while offering the same capabilities as local models, making it possible to keep using your local tools while running larger models that wouldn't fit on a personal computer.

Ollama currently supports the following cloud models, with more coming soon:

- `deepseek-v3.1:671b-cloud`
- `gpt-oss:20b-cloud`
- `gpt-oss:120b-cloud`
- `kimi-k2:1t-cloud`
- `qwen3-coder:480b-cloud`
- `glm-4.6:cloud`
- `minimax-m2:cloud`

### Running Cloud models

Ollama's cloud models require an account on [ollama.com](https://ollama.com). To sign in or create an account, run:

```
ollama signin
```

<Tabs>
  <Tab title="CLI">

To run a cloud model, open the terminal and run:

```
ollama run gpt-oss:120b-cloud
```

  </Tab>
  <Tab title="Python">

First, pull a cloud model so it can be accessed:

```
ollama pull gpt-oss:120b-cloud
```

Next, install [Ollama's Python library](https://github.com/ollama/ollama-python):

```
pip install ollama
```

Next, create and run a simple Python script:

```python
from ollama import Client

client = Client()

messages = [
  {
    'role': 'user',
    'content': 'Why is the sky blue?',
  },
]

for part in client.chat('gpt-oss:120b-cloud', messages=messages, stream=True):
  print(part['message']['content'], end='', flush=True)
```

  </Tab>
  <Tab title="JavaScript">

First, pull a cloud model so it can be accessed:

```
ollama pull gpt-oss:120b-cloud
```

Next, install [Ollama's JavaScript library](https://github.com/ollama/ollama-js):

```
npm i ollama
```

Then use the library to run a cloud model:

```typescript
import { Ollama } from "ollama";

const ollama = new Ollama();

const response = await ollama.chat({
  model: "gpt-oss:120b-cloud",
  messages: [{ role: "user", content: "Explain quantum computing" }],
  stream: true,
});

for await (const part of response) {
  process.stdout.write(part.message.content);
}
```

  </Tab>
  <Tab title="cURL">

First, pull a cloud model so it can be accessed:

```
ollama pull gpt-oss:120b-cloud
```

Run the following cURL command to run the command via Ollama's API:

```
curl http://localhost:11434/api/chat -d '{
  "model": "gpt-oss:120b-cloud",
  "messages": [{
    "role": "user",
    "content": "Why is the sky blue?"
  }],
  "stream": false
}'
```

  </Tab>
</Tabs>

## Cloud API access

Cloud models can also be accessed directly on ollama.com's API. In this mode, ollama.com acts as a remote Ollama host.

### Authentication

For direct access to ollama.com's API, first create an [API key](https://ollama.com/settings/keys).

Then, set the `OLLAMA_API_KEY` environment variable to your API key.

```
export OLLAMA_API_KEY=your_api_key
```

### Listing models

For models available directly via Ollama's API, models can be listed via:

```
curl https://ollama.com/api/tags
```

### Generating a response

<Tabs>
  <Tab title="Python">

First, install [Ollama's Python library](https://github.com/ollama/ollama-python)

```
pip install ollama
```

Then make a request

```python
import os
from ollama import Client

client = Client(
    host="https://ollama.com",
    headers={'Authorization': 'Bearer ' + os.environ.get('OLLAMA_API_KEY')}
)

messages = [
  {
    'role': 'user',
    'content': 'Why is the sky blue?',
  },
]

for part in client.chat('gpt-oss:120b', messages=messages, stream=True):
  print(part['message']['content'], end='', flush=True)
```

  </Tab>
  <Tab title="JavaScript">

First, install [Ollama's JavaScript library](https://github.com/ollama/ollama-js):

```
npm i ollama
```

Next, make a request to the model:

```typescript
import { Ollama } from "ollama";

const ollama = new Ollama({
  host: "https://ollama.com",
  headers: {
    Authorization: "Bearer " + process.env.OLLAMA_API_KEY,
  },
});

const response = await ollama.chat({
  model: "gpt-oss:120b",
  messages: [{ role: "user", content: "Explain quantum computing" }],
  stream: true,
});

for await (const part of response) {
  process.stdout.write(part.message.content);
}
```

  </Tab>
  <Tab title="cURL">

Generate a response via Ollama's chat API:

```
curl https://ollama.com/api/chat \
  -H "Authorization: Bearer $OLLAMA_API_KEY" \
  -d '{
    "model": "gpt-oss:120b",
    "messages": [{
      "role": "user",
      "content": "Why is the sky blue?"
    }],
    "stream": false
  }'
```

  </Tab>
</Tabs>