**LocalAI** is a free, open-source alternative to OpenAI (Anthropic, etc.), functioning as a drop-in replacement REST API for local inferencing. It allows you to run [LLMs]({{% relref "features/text-generation" %}}), generate images, and produce audio, all locally or on-premises with consumer-grade hardware, supporting multiple model families and architectures.
If you are exposing LocalAI remotely, make sure you protect the API endpoints adequately. You have two options:
- **Simple API keys**: Run with `LOCALAI_API_KEY=your-key` to gate access. API keys grant full admin access with no role separation.
- **User authentication**: Run with `LOCALAI_AUTH=true` for multi-user support with admin/user roles, OAuth login, per-user API keys, and usage tracking. See [Authentication & Authorization]({{%relref "features/authentication" %}}) for details.
This guide assumes you have already [installed LocalAI](/installation/). If you haven't installed it yet, see the [Installation guide](/installation/) first.
### Starting LocalAI
Once installed, start LocalAI. For Docker installations:
```bash
docker run -p 8080:8080 --name local-ai -ti localai/localai:latest
```
The API will be available at `http://localhost:8080`.
### Downloading models on start
When starting LocalAI (either via Docker or via CLI) you can specify as argument a list of models to install automatically before starting the API, for example:
```bash
local-ai run llama-3.2-1b-instruct:q4_k_m
local-ai run huggingface://TheBloke/phi-2-GGUF/phi-2.Q8_0.gguf
local-ai run ollama://gemma:2b
local-ai run https://gist.githubusercontent.com/.../phi-2.yaml
local-ai run oci://localai/phi-2:latest
```
{{% notice tip %}}
**Automatic Backend Detection**: When you install models from the gallery or YAML files, LocalAI automatically detects your system's GPU capabilities (NVIDIA, AMD, Intel) and downloads the appropriate backend. For advanced configuration options, see [GPU Acceleration]({{% relref "features/gpu-acceleration#automatic-backend-detection" %}}).
{{% /notice %}}
For a full list of options, you can run LocalAI with `--help` or refer to the [Linux Installation guide]({{% relref "installation/linux" %}}) for installer configuration options.
## Using LocalAI and the full stack with LocalAGI
LocalAI is part of the Local family stack, along with LocalAGI and LocalRecall.
[LocalAGI](https://github.com/mudler/LocalAGI) is a powerful, self-hostable AI Agent platform designed for maximum privacy and flexibility which encompassess and uses all the software stack. It provides a complete drop-in replacement for OpenAI's Responses APIs with advanced agentic capabilities, working entirely locally on consumer-grade hardware (CPU and GPU).
### Quick Start
```bash
git clone https://github.com/mudler/LocalAGI
cd LocalAGI
docker compose up
docker compose -f docker-compose.nvidia.yaml up
docker compose -f docker-compose.intel.yaml up
MODEL_NAME=gemma-3-12b-it docker compose up
MODEL_NAME=gemma-3-12b-it \
MULTIMODAL_MODEL=minicpm-v-4_5 \
IMAGE_MODEL=flux.1-dev-ggml \
docker compose -f docker-compose.nvidia.yaml up
```
### Key Features
- **Privacy-Focused**: All processing happens locally, ensuring your data never leaves your machine
- **Multiple Model Support**: Compatible with various models from Hugging Face and other sources
- **Web Interface**: User-friendly chat interface for interacting with AI agents
- **Advanced Capabilities**: Supports multimodal models, image generation, and more
- **Docker Integration**: Easy deployment using Docker Compose
### Environment Variables
You can customize your LocalAGI setup using the following environment variables:
-`MODEL_NAME`: Specify the model to use (e.g., `gemma-3-12b-it`)
-`MULTIMODAL_MODEL`: Set a custom multimodal model
-`IMAGE_MODEL`: Configure an image generation model
For more advanced configuration and API documentation, visit the [LocalAGI GitHub repository](https://github.com/mudler/LocalAGI).
## What's Next?
There is much more to explore with LocalAI! You can run any model from Hugging Face, perform video generation, and also voice cloning. For a comprehensive overview, check out the [features]({{% relref "features" %}}) section.
Explore additional resources and community contributions:
