llama.cpp vs Ollama

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

Ollama

Open source · MIT

Runs open AI models like Gemma, Qwen and DeepSeek on your own machine or in the cloud.

Full review Website

Our verdict

Pick llama.cpp if you have non-NVIDIA hardware like Apple silicon, want fine control over how models run, or are comfortable building software from source.

Pick Ollama if you want the quickest way to download and chat with open models from a terminal or build an app on a local API without compiling anything.

Key difference	llama.cpp	Ollama
Type	A library/engine you run yourself, written in C/C++	An app with a command line and REST API, written in Go
Setup	Intermediate; may require building from source and picking a backend	Beginner; simple install and run commands on macOS, Windows, Linux and Docker
Hardware support	Wide range of CPUs and GPUs, including Apple silicon and AMD	README does not state hardware requirements or model sizes
Best at	Running models on varied hardware with quantization to cut memory use	Easy local model access and connecting apps or coding assistants
Cloud option	None; it is software you run yourself	README mentions cloud access, depending on Ollama's own services

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 Ollama if you need…

Developers who want a local model API for their own apps
People who want to chat with open models without sending prompts to a hosted service
Users who want to connect a coding assistant to a local or self-chosen model
Students experimenting with different open models from a terminal

Side-by-side

	llama.cpp	Ollama
Type	Library	App
Price	Free and open source	Free and open source
Licence	MIT	MIT
GitHub stars	133k	186k
Setup difficulty	Intermediate	Beginner
How you get it	See the README	Install with a package manager
Platforms	—	macOS, Windows, Linux, CLI, API
Works with	—	VS Code
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.

Ollama

Ollama is a tool for downloading and running open-weight AI language models locally, with a simple command line and a REST API. The README says it also offers cloud access and can connect to coding tools and assistants such as Claude Code, Codex and OpenClaw. People use it to try models privately or to build apps that call a model through a local API.

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

Ollama

Developers who want a local model API for their own apps
People who want to chat with open models without sending prompts to a hosted service
Users who want to connect a coding assistant to a local or self-chosen model
Students experimenting with different open models from a terminal

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

Ollama

Simple install and run commands across macOS, Windows, Linux and Docker
Official Python and JavaScript libraries plus a documented REST API
Large community with many integrations listed in the README

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

Ollama

People who need the most capable closed frontier models, since this focuses on open models
Users with limited disk space or memory, because model files can be large and need capable hardware (the README does not give specific requirements)
Non-technical users who want a polished desktop app with no terminal use, although the README mentions a download option for macOS and Windows

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

Ollama

The README is truncated and does not state hardware requirements or model sizes
Some features, such as cloud access and specific integrations, depend on Ollama's own services and documentation that is only partly described here
Model quality and licensing depend on each individual model, not on Ollama itself

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

Ollama

macOS and Linux install

curl -fsSL https://ollama.com/install.sh | sh

Windows install (PowerShell)

irm https://ollama.com/install.ps1 | iex

Step-by-step guide: how to use Ollama

Frequently asked questions

Should I use llama.cpp or Ollama?

llama.cpp is a low-level open-source engine for running AI language models on your own hardware, and it rewards technical users who want control over backends and performance. Ollama is a friendlier app-style tool built on the same idea of running open models locally, with simple install commands and a local API for building apps. Both are free and MIT-licensed, so the choice mostly comes down to how much setup and tinkering you are willing to do. Pick llama.cpp if you have non-NVIDIA hardware like Apple silicon, want fine control over how models run, or are comfortable building software from source. Pick Ollama if you want the quickest way to download and chat with open models from a terminal or build an app on a local API without compiling anything.

What is the difference between llama.cpp and Ollama?

llama.cpp: Runs large language models locally in plain C/C++, on CPUs and many kinds of GPUs. Ollama: Runs open AI models like Gemma, Qwen and DeepSeek on your own machine or in the cloud. llama.cpp suits developers who want to run open-weight models locally without a cloud api, while Ollama suits developers who want a local model api for their own apps.

Are llama.cpp and Ollama free?

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

Which is easier to set up, llama.cpp or Ollama?

Ollama is easier to get started with (rated beginner); llama.cpp is rated intermediate and usually needs more setup.

Is llama.cpp or Ollama more popular?

Ollama has more GitHub stars (186k) than llama.cpp (133k). Popularity isn't the same as fit — check which one matches your use case above.

What are other alternatives to llama.cpp and Ollama?

Other options people compare them with include MindsHub, Reasonix, magpie, LocalAI, LlamaGPT.

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.