> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.privategpt.dev/providers/ollama/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.privategpt.dev/_mcp/server.

# Ollama

> Run local LLMs with Ollama — the easiest way to get started with PrivateGPT.

[Ollama](https://ollama.ai) lets you run large language models locally with a single command. It handles model downloading, GPU offloading, and serving an OpenAI-compatible API on port `11434`.

## Limitations with PrivateGPT

> **Warning**
>
> Ollama does **not** expose a tokenizer endpoint (`/tokenize`). PrivateGPT falls back to a character-based estimate (4 chars = 1 token) for token counting. This can cause context-window overflow on long inputs.
>
> **Recommendation:** Set `context_window` explicitly in a [detailed model profile](/configuration/advanced) to match your model's actual limit. By default, Ollama assumes the following context window based on the VRAM:
>
> \< 24 GiB VRAM: 4k context
> 24-48 GiB VRAM: 32k context
>
> > \= 48 GiB VRAM: 256k context

| Capability                       | Status            |
| -------------------------------- | ----------------- |
| Model discovery (`/v1/models`)   | ✅                 |
| Tokenizer endpoint (`/tokenize`) | ❌                 |
| Embeddings                       | ✅                 |
| Tool / function calling          | ✅ model-dependent |
| Structured output                | ❌                 |
| Streaming                        | ✅                 |
| Vision / image input             | ✅ model-dependent |

---

## Setup

#### Install Ollama

Download and install from [ollama.ai](https://ollama.ai) for your platform (macOS, Linux, Windows).

Or on macOS:

```bash
brew install ollama
```

#### Pull a model

```bash
# Example LLM — Qwen3.5 35B (~24 GB)
ollama pull qwen3.5:35b

# Example embeddings model (~670 MB)
ollama pull mxbai-embed-large
```

> **Note**
>
> Any model from the [Ollama library](https://ollama.com/library) works. For smaller hardware, try `qwen3.5:7b`.

#### Start the Ollama server

```bash
ollama serve
```

The API is now available at `http://localhost:11434/v1`.

> **Tip**
>
> On macOS, the Ollama desktop app starts the server automatically when open. You don't need to run `ollama serve` manually.

#### Run PrivateGPT

#### Package install

```bash
OPENAI_API_BASE=http://localhost:11434/v1 private-gpt serve
```

#### Docker

```bash
docker run -p 8080:8080 \
  -e OPENAI_API_BASE=http://host.docker.internal:11434/v1 \
  zylonai/private-gpt:latest
```

#### uv (local)

```bash
OPENAI_API_BASE=http://localhost:11434/v1 uv run private-gpt serve
```

---

## Advanced profile example

Because Ollama lacks the tokenizer endpoint, it's especially useful to set `context_window` explicitly:

```yaml
# settings-model.yaml
llm:
  default_model: qwen3.5:35b

embedding:
  default_model: mxbai-embed-large

models:
  - name: qwen3.5:35b
    type: llm
    mode: openai
    context_window: 32768        # Set explicitly — Ollama can't report this
    support_tools: true
    support_reasoning: true
    sampling_params:
      temperature: 0.6
      top_p: 0.95
      top_k: 20
      min_p: 0.0

  - name: mxbai-embed-large
    type: embedding
    mode: openai
    context_window: 512
```

Generate this automatically with:

```bash
OPENAI_API_BASE=http://localhost:11434/v1 \
  uv run python scripts/auto_discover_models.py --out settings-model.yaml
```

Then edit `context_window` and other values as needed and run:

```bash
OPENAI_API_BASE=http://localhost:11434/v1 \
  PGPT_PROFILES=model \
  uv run python -m private_gpt
```

---

## Troubleshooting

**Connection refused inside Docker**

Use `host.docker.internal` instead of `localhost`:

```bash
-e OPENAI_API_BASE=http://host.docker.internal:11434/v1
```

On Linux with Docker, use `--network host` instead:

```bash
docker run --network host -e OPENAI_API_BASE=http://localhost:11434/v1 ...
```

**Model not found**

Verify the model is available:

```bash
ollama list
```

> Run local LLMs with Ollama — the easiest way to get started with PrivateGPT.