Model components / Inference code

llama.cpp

llama.cpp runs LLM and VLM inference in portable, dependency-free C/C++, across CPUs and GPU backends including CUDA, Metal, Vulkan, SYCL, and HIP. It supports 1.5-bit through 8-bit integer quantization and the GGUF format used widely for local model distribution, from servers down to Android phones. The ggml-org organization maintains it, and its ggml.ai team joined Hugging Face in February 2026.

The prior claim that llama.cpp underpins Ollama, LM Studio, and GPT4All no longer appears in the README, so it was dropped; the Hugging Face Hub documents those tools' GGUF support separately. Verified 2026-08-09 via GitHub, the LICENSE body, and the Hugging Face GGUF documentation.

Openness

5 high confidence

5.0

license: MIT(OSI)
source: public
governance: ggml-org/Hugging Face(community)
core-gated: ungated

Fully MIT-licensed C/C++ inference engine: the entire source is public and there is no proprietary tier. Neither the repository nor the ggml-org GitHub org profile offers a hosted or paid SKU, so the only distribution is the one MIT-licensed codebase.

https://github.com/ggml-org/llama.cpp/blob/master/LICENSE recorded 2026-08-09
MIT License text, "Copyright (c) 2023-2026 The ggml authors"
https://github.com/ggml-org/llama.cpp recorded 2026-08-09
Public repository, single MIT license badge, README states "Plain C/C++ implementation without any dependencies"; no pricing page, paid tier, or feature-gated build found in the repo

Adoption

4 medium confidence

4.0

The de facto standard local and edge inference engine. The Hugging Face Hub documents GGUF usage separately for llama.cpp, Ollama, LM Studio and GPT4All, which reflects how central the format has become across that ecosystem. No clean PyPI or download count exists for the C++ engine itself, so usage is inferred from that ecosystem centrality and from steadily growing GitHub stars, now 123,185, rather than from a direct install count; that puts it at level 4, the 1M-10M equivalent band. A widely repeated claim that GGUF accounts for more than 60% of all quantized models on Hugging Face is not carried here, because no cited source states it.

https://github.com/ggml-org/llama.cpp recorded 2026-08-09
"123185 users starred" this repository
https://huggingface.co/docs/hub/gguf-llamacpp recorded 2026-08-09
Separate Hub doc pages titled "GGUF usage with llama.cpp", "GGUF usage with LM Studio", "GGUF usage with GPT4All", and "GGUF usage with Ollama"

Capability

4 high confidence

4.0

The frontier for portable, CPU and consumer-hardware inference and for quantization breadth, rather than the datacenter-GPU throughput frontier occupied by vLLM, SGLang and TensorRT-LLM. That is what places it at 4 rather than 5.

https://github.com/ggml-org/llama.cpp recorded 2026-08-09
Backend list (Metal/CUDA/HIP/MUSA/Vulkan/SYCL/WebGPU/OpenVINO), 1.5-8bit quantization list, multimodal subsystem reference, "llama-server REST API", OpenAI-compatible server example
https://github.com/ggml-org/llama.cpp/blob/master/docs/models.md recorded 2026-08-09
"The Hugging Face platform hosts [thousands of models]" compatible with llama.cpp

Verified 2026-08-09