* docs: add 'how LocalAI works' architecture diagram Add a blueprint-style architecture diagram: clients -> small core (API, router, WebUI, agents) -> gRPC -> backend processes pulled on demand as OCI images. Place it on the overview page and replace the stale external architecture image on the reference page. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: add blueprint diagrams across feature, distributed & getting-started docs Add 24 architecture/flow/comparison diagrams (PNG + HTML source) under docs/static/images/diagrams/, wired into their docs pages, from an impact-vs-effort audit of the docs. Broaden the API surface on the overview architecture diagram (OpenAI, Anthropic, ElevenLabs, Ollama, and LocalAI's own API) and move the gRPC boundary label clear of the arrows. Pages: distributed mode (architecture, scheduling, ds4 layer-split), distributed inferencing, MLX, realtime, quantization, MCP, agents, mitm & cloud proxy, middleware, reverse-proxy TLS, VRAM, voice & face recognition, reranker, function calling, fine-tuning (recipe + jobs), diarization, audio transform, quickstart, model resolution. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: add composable-core diagram to README hero Commit the composable-core card (small core + on-demand backend tiles) alongside the other diagrams and reference it from the README hero via a repo-relative path, so it renders on GitHub. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: fix composable-core connectors/badge and federated-vs-worker layout - composable-core: thicken the plug-in connectors so they read clearly, and widen the SEPARATE IMAGE badge so its text no longer overflows the box. - federated-vs-worker: shorten the WHOLE/SPLIT REQUEST pills to fit, and replace the tangled node-to-node activation arrows with a clean fan-out (request split across all sharded nodes), mirroring the federated panel. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
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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.
LocalAI comes with a built-in web interface for chatting with models, managing installations, configuring AI agents, and more — no extra tools needed.
{{% notice tip %}}
Security considerations
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-keyto gate access. API keys grant full admin access with no role separation. - User authentication: Run with
LOCALAI_AUTH=truefor multi-user support with admin/user roles, OAuth login, per-user API keys, and usage tracking. See [Authentication & Authorization]({{%relref "features/authentication" %}}) for details.
{{% /notice %}}
Quickstart
This guide assumes you have already installed LocalAI. If you haven't installed it yet, see the Installation guide first.
Starting LocalAI
Once installed, start LocalAI. For Docker installations:
docker run -p 8080:8080 --name local-ai -ti localai/localai:latest-cpu
For GPU acceleration, choose the image that matches your hardware:
| Hardware | Docker image |
|---|---|
| CPU only | localai/localai:latest-cpu |
| NVIDIA CUDA | localai/localai:latest-gpu-nvidia-cuda-12 |
| AMD (ROCm) | localai/localai:latest-gpu-hipblas |
| Intel GPU | localai/localai:latest-gpu-intel |
| Vulkan | localai/localai:latest-gpu-vulkan |
For NVIDIA GPUs, add --gpus all. For AMD/Intel/Vulkan, add the appropriate --device flags. See [Container images]({{% relref "getting-started/container-images" %}}) for the full reference.
Using the Web Interface
Open http://localhost:8080 in your browser. The web interface lets you:
- Chat with any installed model
- Install models from the built-in gallery (Models page)
- Generate images, audio, and more
- Create and manage AI agents with MCP tool support
- Monitor system resources and loaded models
- Configure settings including GPU acceleration
To get started, navigate to the Models page, browse the gallery, and install a model. Once installed, head to the Chat page to start a conversation.
Downloading models from the CLI
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:
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
You can also manage models with the CLI:
local-ai models list # List available models in the gallery
local-ai models install <name> # Install a model
{{% 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 the API
LocalAI exposes an OpenAI-compatible API. You can use it with any OpenAI SDK or client by pointing it to http://localhost:8080. For example:
curl http://localhost:8080/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "llama-3.2-1b-instruct:q4_k_m",
"messages": [{"role": "user", "content": "Hello!"}]
}'
LocalAI also supports the Anthropic Messages API, the Open Responses API, and more. See [Try it out]({{% relref "getting-started/try-it-out" %}}) for examples of all supported endpoints.
Built-in AI Agents
LocalAI includes a built-in AI agent platform with support for the Model Context Protocol (MCP). You can create agents that use tools, browse the web, execute code, and interact with external services — all from the web interface.
To get started with agents:
- Install a model that supports tool calling (most modern LLMs do)
- Navigate to the Agents page in the web interface
- Create a new agent, configure its tools and system prompt
- Start chatting — the agent will use tools autonomously
No separate installation required — agents are part of LocalAI.
Scaling with Distributed Mode
For production deployments or when you need more compute, LocalAI supports distributed mode with horizontal scaling:
- Distributed nodes: Add GPU worker nodes that self-register with a frontend coordinator
- P2P federation: Connect multiple LocalAI instances for load-balanced inference
- Model sharding: Split large models across multiple machines
See the Nodes page in the web interface or the [Distribution docs]({{% relref "features/distribution" %}}) for setup instructions.
What's Next?
There is much more to explore! LocalAI supports video generation, voice cloning, embeddings, image understanding, and more. Check out:
- [Container images reference]({{% relref "getting-started/container-images" %}})
- [Try the API endpoints]({{% relref "getting-started/try-it-out" %}})
- [All features]({{% relref "features" %}})
- Model gallery
- [Run models manually]({{% relref "getting-started/models" %}})
- [Build from source]({{% relref "installation/build" %}})
- Examples
