llama.cpp vs 
exo
llama.cpp vs exo compared for 2026 — features, license, ease of use, performance and which one to choose. The C/C++ engine powering local inference vs Run big models across your everyday devices.
Updated regularly · curated by OpenSourceAI.tech
llama.cpp vs exo at a glance
| Spec | llama.cpp | exo |
|---|---|---|
| Category | Run LLMs locally | Run LLMs locally |
| Type | Inference library (C/C++) | Distributed home cluster |
| License | MIT | GPL-3.0 |
| Runs locally | Yes | Yes |
| Primary language | C/C++ | Python |
| Ease of use | Advanced | Intermediate |
| Best for | developers who want maximum control and portability | running models too large for any single machine at home |
| GitHub stars | 120.6k | — |
Feature comparison
| Feature | llama.cpp | exo |
|---|---|---|
| Runs locally | ✓ | ✓ |
| Graphical UI | ✗ | ✗ |
| OpenAI-compatible API | ✓ | ✓ |
| Docker | ✓ | ✗ |
| GPU acceleration | ✓ | ✓ |
| Built-in model library | ✗ | ✓ |
How llama.cpp and exo score
| Criterion | llama.cpp | exo |
|---|---|---|
| Popularity | 5.0 | n/a |
| Maintenance | 5.0 | n/a |
| Ease of use | 2.5 | 3.5 |
| Privacy | 5.0 | 5.0 |
| License freedom | 5.0 | 3.5 |
Scores are computed automatically from public signals — GitHub stars (popularity), recent commit activity (maintenance), license type (freedom), local-first design (privacy) and onboarding complexity (ease of use). Indicative, not a verdict.
What each one is
llama.cpp
llama.cpp is the high-performance C/C++ inference engine that underpins most local LLM tools, supporting GGUF models with aggressive quantization across CPUs and GPUs.
- Runs almost anywhere, from laptops to Raspberry Pi
- State-of-the-art quantization (GGUF) for tiny footprints
- The engine many other tools are built on top of
exo
exo turns the devices you already own — Macs, PCs, phones — into a self-organizing AI cluster, splitting large models across them with automatic peer discovery.
- Aggregates the memory of all your devices automatically
- ChatGPT-compatible API on your own cluster
- No expensive GPU server needed for large models
Key differences
llama.cpp is inference library (C/C++), while exo is distributed home cluster. Their licenses differ (MIT vs GPL-3.0), which matters if you ship a commercial product. llama.cpp leans more advanced-friendly, whereas exo is more suited to intermediate users. In short, llama.cpp fits developers who want maximum control and portability, and exo fits running models too large for any single machine at home.
Which should you choose?
Choose llama.cpp for developers who want maximum control and portability. Choose exo for running models too large for any single machine at home.
There is rarely one winner — many setups use both. The right pick depends on your hardware, your team's skills, and whether you value simplicity or control.
Frequently asked questions
Is llama.cpp or exo easier to use?
exo is generally the easier of the two to get started with, while llama.cpp rewards more setup with more control.
Are llama.cpp and exo free?
llama.cpp is free and open source (MIT), and exo is free and open source (GPL-3.0). Neither charges for the core software.
Can I run llama.cpp and exo locally?
llama.cpp: yes · exo: yes. Both can be used without sending your data to a third-party cloud where their setup allows.
llama.cpp vs exo — which should I pick in 2026?
Choose llama.cpp for developers who want maximum control and portability. Choose exo for running models too large for any single machine at home.
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