Open-Source AI · Run LLMs locally

llama.cpp vs KoboldCpp

llama.cpp vs KoboldCpp compared for 2026 — features, license, ease of use, performance and which one to choose. The C/C++ engine powering local inference vs Single-file local model runner.

Updated regularly · curated by olud.ai

Choose llama.cpp for developers who want maximum control and portability. Choose KoboldCpp for one-file local inference with a UI.

llama.cpp vs KoboldCpp at a glance

Spec	llama.cpp	KoboldCpp
Category	Run LLMs locally	Run LLMs locally
Type	Inference library (C/C++)	Local runtime (single file)
License	MIT	AGPL-3.0
Runs locally	Yes	Yes
Primary language	C/C++	C++
Ease of use	Advanced	Beginner
Best for	developers who want maximum control and portability	one-file local inference with a UI
GitHub stars	121.2k	—

How llama.cpp and KoboldCpp score

🤝 Too close to call — llama.cpp and KoboldCpp land within a hair (4.5 vs 4.5 / 5). Pick on fit, not on score.

Criterion	llama.cpp	KoboldCpp
Popularity	5.0	n/a
Maintenance	5.0	n/a
Ease of use	2.5	5.0
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

Inference library (C/C++) · MIT

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

See the llama.cpp page →

KoboldCpp

Local runtime (single file) · AGPL-3.0

KoboldCpp is an easy, single-executable way to run GGUF models locally with a built-in UI, strong sampler controls and support for text, image and voice.

Single executable, no install
Built-in UI and API
Great sampler and context controls

Visit KoboldCpp →

Key differences

llama.cpp is inference library (C/C++), while KoboldCpp is local runtime (single file). Their licenses differ (MIT vs AGPL-3.0), which matters if you ship a commercial product. llama.cpp leans more advanced-friendly, whereas KoboldCpp is more suited to beginner users. In short, llama.cpp fits developers who want maximum control and portability, and KoboldCpp fits one-file local inference with a UI.

Which should you choose?

Choose llama.cpp for developers who want maximum control and portability. Choose KoboldCpp for one-file local inference with a UI.

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 KoboldCpp easier to use?

KoboldCpp is generally the easier of the two to get started with, while llama.cpp rewards more setup with more control.

Are llama.cpp and KoboldCpp free?

llama.cpp is free and open source (MIT), and KoboldCpp is free and open source (AGPL-3.0). Neither charges for the core software.

Can I run llama.cpp and KoboldCpp locally?

llama.cpp: yes · KoboldCpp: yes. Both can be used without sending your data to a third-party cloud where their setup allows.

llama.cpp vs KoboldCpp — which should I pick in 2026?

Choose llama.cpp for developers who want maximum control and portability. Choose KoboldCpp for one-file local inference with a UI.

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