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

# Quickstart

> Get PrivateGPT running in under 5 minutes.

PrivateGPT connects to any OpenAI-compatible LLM server and exposes a private, self-hosted AI API. This guide gets you from zero to a running server in four steps.

> **Note**
>
> **Prerequisites:** You need an OpenAI-compatible LLM server running locally. Pick one from the [Providers](/providers/overview) page — [Ollama](/providers/ollama) is the easiest way to start.

#### Install PrivateGPT

#### Linux

```bash
# Install uv first
curl -LsSf https://astral.sh/uv/install.sh | sh

# Then install PrivateGPT
uv tool install --python 3.11 \
  --find-links https://wheels.privategpt.dev/packages/ \
  "private-gpt[core]"
```

#### macOS

```bash
brew tap zylon-ai/tap
brew install private-gpt
```

#### Windows

```powershell
# Install uv first
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

# Then install PrivateGPT
uv tool install --python 3.11 `
  --find-links https://wheels.privategpt.dev/packages/ `
  "private-gpt[core]"
```

#### Start your LLM server

Start your server. PrivateGPT auto-discovers all available models on startup.

#### Ollama

```bash
# Example: pull a model and start the server
ollama pull qwen3.5:35b          # LLM (~24 GB)
ollama pull mxbai-embed-large   # Embeddings (~670 MB)

# Start the server (runs on port 11434)
ollama serve
```

> **Warning**
>
> Ollama does not expose a tokenizer endpoint. PrivateGPT falls back to approximate token counting, which may affect context-window management. See [Ollama limitations](/providers/ollama#limitations).

#### LM Studio

1. Download and install [LM Studio](https://lmstudio.ai).
2. Load a model (e.g. `Qwen3-35B-A3B`).
3. In **Developer → Local Server**, set the chat model to your LLM and the **Embedding model** to something like `mxbai-embed-large`.
4. Click **Start Server** (default port: 1234).

See the [LM Studio provider guide](/providers/lmstudio) for full setup.

#### LlamaCPP Server

```bash
# Start the LLM server
llama-server \
  --model qwen3-35b-a3b.gguf \
  --port 8000

# Start a second server for embeddings
llama-server \
  --model mxbai-embed-large-v1-f16.gguf \
  --port 8001 \
  --embeddings
```

See the [LlamaCPP provider guide](/providers/llamacpp) for download and build instructions.

#### vLLM

```bash
# Start the LLM server
docker run --gpus all \
  -p 8000:8000 \
  vllm/vllm-openai:latest \
  --model Qwen/Qwen3.5-35B-A3B-GPTQ-Int4

# Start a second server for embeddings
docker run --gpus all \
  -p 8001:8000 \
  vllm/vllm-openai:latest \
  --model mixedbread-ai/mxbai-embed-large-v1 \
  --task embed
```

See the [vLLM provider guide](/providers/vllm) for full setup. Requires an NVIDIA GPU.

#### Run PrivateGPT

Point PrivateGPT at your servers with `OPENAI_API_BASE` and `OPENAI_EMBEDDING_API_BASE`. Models are discovered automatically — no config file needed.

#### macOS / Linux

```bash
OPENAI_API_BASE=http://localhost:<llm-port>/v1 \
  OPENAI_EMBEDDING_API_BASE=http://localhost:<embedding-port>/v1 \
  private-gpt serve
```

#### Windows (PowerShell)

```powershell
$env:OPENAI_API_BASE = "http://localhost:<llm-port>/v1"
$env:OPENAI_EMBEDDING_API_BASE = "http://localhost:<embedding-port>/v1"
private-gpt serve
```

#### Windows (CMD)

```cmd
set OPENAI_API_BASE=http://localhost:<llm-port>/v1
set OPENAI_EMBEDDING_API_BASE=http://localhost:<embedding-port>/v1
private-gpt serve
```

If startup succeeds, PrivateGPT will be available on port `8080`.

#### Open the UI

Navigate to [http://localhost:8080/ui](http://localhost:8080/ui) in your browser.

The API is available at `http://localhost:8080` and follows the Anthropic API spec. See the [API Reference](/api-reference/api-reference) for all endpoints.

---

## What's next?

> **Note**
>
> If you plan to use database querying or web search tools, review the dependency guides in [Database Tools](/tools/database-tools) and [Web Tools](/tools/web-tools) to install the required drivers, OS libraries, and browser dependencies.

#### [Docker install](/installation/docker)

Run PrivateGPT with Docker for a fully isolated, production-ready setup.

#### [Local with uv](/installation/local)

Install from source with `core`, add extras only when needed, and use detailed model configuration.

#### [Inference Providers](/providers/overview)

Compare Ollama, LM Studio, LlamaCPP, and vLLM — feature matrix and limitations.

#### [API Reference](/api-reference/api-reference)

Explore all REST endpoints and start building your application.

> Get PrivateGPT running in under 5 minutes.