llama.cpp vs LocalAI

Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.

llama.cpp

Open source · MIT

Runs large language models locally in plain C/C++, on CPUs and many kinds of GPUs.

Full review Website

LocalAI

Open source · MIT

Open-source engine that runs AI models (text, vision, voice, image, video) locally behind an OpenAI-style API.

Full review Website

Our verdict

Pick llama.cpp if you want to run language models directly on your own machine, especially on Apple silicon or other non-NVIDIA hardware, and you are comfortable with command-line setup.

Pick LocalAI if you want a self-hosted, OpenAI-compatible server that handles several kinds of AI models, such as text, voice and image, behind one API.

Key difference	llama.cpp	LocalAI
Type	A library for running LLMs on your hardware	A self-hosted server with an OpenAI-style API
Model scope	Focused on language and vision-language models	Covers text, vision, voice, image and video models
Hardware	Strong on Apple silicon and many GPU and CPU types	Supports NVIDIA, AMD, Intel, Apple Silicon and CPU-only
Setup	Intermediate; may need building from source or picking a backend	Intermediate; often run with Docker, but many features to configure
Disk use	Quantized models help reduce memory needs	Models and backends download on demand and can be large

Written with AI from both products' catalogue entries · updated 2026-10-29

Who each one is for

Choose llama.cpp if you need…

Developers who want to run open-weight models locally without a cloud API
People with Apple silicon or other non-NVIDIA hardware who want GPU-accelerated inference
Teams that want a self-hosted OpenAI-compatible endpoint
Researchers experimenting with quantized models on limited memory

Choose LocalAI if you need…

Developers who want an OpenAI-compatible endpoint running on their own machines or servers
Teams that need privacy and want data to stay inside their infrastructure
People experimenting with several model types (text, voice, image) behind one API
Users with mixed hardware, such as NVIDIA, AMD, Intel, Apple Silicon or CPU-only machines

Side-by-side

	llama.cpp	LocalAI
Type	Library	App
Price	Free and open source	Free and open source
Licence	MIT	MIT
GitHub stars	133k	50.1k
Setup difficulty	Intermediate	Intermediate
How you get it	See the README	Run with Docker
Main language	C++	Go
Last code update	4 days ago	4 days ago

What each one is

llama.cpp

llama.cpp is an open-source project for running large language models (LLMs), and vision-language models, on your own hardware. It is written in C/C++ with no required dependencies and supports many chip types, including Apple silicon, x86 and RISC-V CPUs, and NVIDIA, AMD and other GPUs. People use it to run models locally or on their own servers, with a command-line tool, a built-in web UI, and an OpenAI-compatible API server.

LocalAI

LocalAI is a self-hosted server that runs many kinds of AI models on your own hardware, including language, vision, voice, image and video models. It exposes an API that is compatible with OpenAI, Anthropic and ElevenLabs, so existing tools can point at it instead of a cloud service. The README says it can run without a GPU and keeps data on your own infrastructure.

Best for

llama.cpp

Developers who want to run open-weight models locally without a cloud API
People with Apple silicon or other non-NVIDIA hardware who want GPU-accelerated inference
Teams that want a self-hosted OpenAI-compatible endpoint
Researchers experimenting with quantized models on limited memory

LocalAI

Developers who want an OpenAI-compatible endpoint running on their own machines or servers
Teams that need privacy and want data to stay inside their infrastructure
People experimenting with several model types (text, voice, image) behind one API
Users with mixed hardware, such as NVIDIA, AMD, Intel, Apple Silicon or CPU-only machines

Strengths

llama.cpp

Plain C/C++ with no required dependencies, and a wide range of supported hardware backends
Quantization options from 1.5-bit to 8-bit reduce memory needs
Large, active open-source project under the MIT license

LocalAI

Broad model support across text, vision, voice, image and video, per the README
Works with several hardware types, including CPU-only setups
API compatibility with OpenAI, Anthropic and ElevenLabs reduces code changes

Not ideal for

llama.cpp

Users who want a polished consumer chat app with no setup; the README points to command-line and build steps
People who need a managed cloud service, since this is software you run yourself
Users with very limited RAM or VRAM, since performance depends heavily on hardware and model size

LocalAI

People who only want a simple desktop chat app; the README focuses on a server and API, though a macOS launcher exists
Users with very limited disk space, since models and backends are downloaded on demand and can be large
Those who need a fully managed cloud service with no setup work

Trade-offs

llama.cpp

Setup can require building from source or choosing the right backend for your hardware
Performance and supported features vary by backend and device, and some backends are marked in progress
Results depend on local hardware, so the README's performance claims are hard to verify for your machine

LocalAI

Many features and backends make the project complex to configure; the README is long and the docs are external
Backends are downloaded on demand, so first runs may take time and bandwidth
The macOS DMG is not signed by Apple, requiring a manual quarantine-removal step

Getting started

llama.cpp

Install with curl (macOS/Linux installer from README)

curl -LsSf https://llama.app/install.sh | sh

Install with PowerShell (Windows installer from README)

irm https://llama.app/install.ps1 | iex

Step-by-step guide: how to use llama.cpp

LocalAI

Run CPU-only container

docker run -ti --name local-ai -p 8080:8080 localai/localai:latest

Run with NVIDIA GPU (CUDA 12)

docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-gpu-nvidia-cuda-12

Step-by-step guide: how to use LocalAI

Frequently asked questions

Should I use llama.cpp or LocalAI?

llama.cpp is a lean, low-level engine (a library) for running language models on your own hardware, with strong support for Apple silicon and non-NVIDIA GPUs, and it suits developers comfortable building software. LocalAI is a broader self-hosted server that runs text, voice, image and video models behind an OpenAI-style API, and suits teams who want one endpoint for many model types and their data kept in-house. Both are free and MIT-licensed, so the choice comes down to how narrow or broad your needs are. Pick llama.cpp if you want to run language models directly on your own machine, especially on Apple silicon or other non-NVIDIA hardware, and you are comfortable with command-line setup. Pick LocalAI if you want a self-hosted, OpenAI-compatible server that handles several kinds of AI models, such as text, voice and image, behind one API.

What is the difference between llama.cpp and LocalAI?

llama.cpp: Runs large language models locally in plain C/C++, on CPUs and many kinds of GPUs. LocalAI: Open-source engine that runs AI models (text, vision, voice, image, video) locally behind an OpenAI-style API. llama.cpp suits developers who want to run open-weight models locally without a cloud api, while LocalAI suits developers who want an openai-compatible endpoint running on their own machines or servers.

Are llama.cpp and LocalAI free?

llama.cpp: Free and open source. LocalAI: Free and open source.

Is llama.cpp or LocalAI more popular?

llama.cpp has more GitHub stars (133k) than LocalAI (50.1k). Popularity isn't the same as fit — check which one matches your use case above.

What are other alternatives to llama.cpp and LocalAI?

Other options people compare them with include Ollama, LlamaGPT, Toonflow, GPT-SoVITS.

Keep comparing

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Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.