LocalAI: Self-Hosted AI Model Server

LocalAI is a drop-in replacement REST API compatible with OpenAI's API specifications, allowing you to run large language models, image generation, audio transcription, and embedding models locally on your own hardware. With over 42,000 GitHub stars, LocalAI lets you use open-source AI models with the same API calls you'd use for OpenAI — just point your application to your LocalAI server instead.

Self-hosting LocalAI means your prompts, responses, and data never leave your server. No API keys, no per-token billing, no data shared with cloud AI providers.

Key Features

• OpenAI-compatible REST API for text generation, chat completions, embeddings, and more
• Support for running LLMs including Llama, Mistral, Phi, and other open-source models
• Image generation with Stable Diffusion models served through the same API
• Audio transcription with Whisper models for speech-to-text capabilities
• Text-to-speech generation for converting text into natural-sounding audio
• GPU acceleration support for NVIDIA CUDA, AMD ROCm, and Apple Metal
• Model gallery for one-click download and configuration of popular models
• No internet connection required after model download — fully air-gapped operation

Why Self-Host LocalAI?

Complete data privacy. Every prompt you send to cloud AI services is processed on their servers. Self-hosted LocalAI processes everything locally — customer data, proprietary documents, code, and conversations never leave your infrastructure. This is essential for sensitive business applications.

No per-token costs. OpenAI, Anthropic, and other AI providers charge per token. For applications with high inference volume — chatbots, document processing, code generation — costs add up fast. Self-hosted LocalAI has zero marginal cost per request after the server is running.

API compatibility. LocalAI implements the OpenAI API specification, so existing applications and libraries that work with OpenAI can switch to LocalAI by changing a single URL. No code rewrites needed — just redirect your API calls.

Model freedom. Run any open-source model that fits your use case — uncensored models for research, specialized models for your domain, or fine-tuned models trained on your data. You choose what runs on your hardware without vendor restrictions.

System Requirements

Resource	Minimum	Recommended
CPU	4 vCPUs	8+ vCPUs
RAM	8 GB	16 GB
Storage	20 GB SSD	100 GB SSD
OS	Ubuntu 22.04+	Ubuntu 24.04

AI model inference is resource-intensive. RAM requirements depend on model size — a 7B parameter model needs approximately 4-8 GB RAM, while larger models need proportionally more. GPU acceleration dramatically improves inference speed. Storage requirements scale with the number and size of downloaded models.

Getting Started

The fastest way to deploy LocalAI on your VPS is with Docker Compose through Dokploy. Our step-by-step deployment guide walks you through the full setup, including persistent storage, environment configuration, and SSL.

Deploy LocalAI with Dokploy →

Alternatives

• Ollama — Simplified local LLM runner with a streamlined CLI and model management
• vLLM — High-throughput LLM serving engine optimized for production inference workloads
• llama.cpp — Efficient C++ inference engine for running LLMs on consumer hardware
• Open WebUI — Chat interface for local AI models with conversation management and RAG support

FAQ

Can LocalAI run on a VPS without a GPU? Yes. LocalAI supports CPU-only inference using optimized backends like llama.cpp. Performance is slower than GPU inference, but small to medium models (7B-13B parameters) run usably on modern CPUs with enough RAM. Quantized models (GGUF format) are recommended for CPU inference.

How does LocalAI compare to Ollama? LocalAI provides a broader API surface — it supports text generation, image generation, audio transcription, embeddings, and TTS through a single OpenAI-compatible API. Ollama focuses specifically on LLM chat inference with a simpler interface. LocalAI is better for applications that need a full OpenAI API replacement.

What models can I run with LocalAI? LocalAI supports models in GGUF, GGML, and other common formats. Popular models include Llama 3, Mistral, Phi, Code Llama, and Stable Diffusion. The model gallery provides pre-configured setups for many popular models. Any model compatible with the supported backends can be loaded.

Is LocalAI suitable for production applications? Yes, with appropriate hardware. For production use, GPU acceleration is recommended for acceptable latency. LocalAI can serve multiple concurrent requests and supports model loading/unloading for efficient resource usage. Monitor RAM and CPU usage to ensure your server handles your expected request volume.

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App data sourced from selfh.st open-source directory.