Blame for README.md

# LLAMA_CPP



![licence](https://img.shields.io/badge/licence-MIT-blue) ![offline-first](https://img.shields.io/badge/offline--first-air--gap-green) ![audit](https://img.shields.io/badge/audit-SHA3--256-orange) ![checks](https://img.shields.io/badge/checks-unknown_PASS-brightgreen)



> Governed Anticloud packaging of the upstream project `LLAMA_CPP` in category **FRONTIER_HARNESSES**. check results: see ISOLATED_LAB_RESULTS. Every number below traces to a named file + run stamp; nothing is borrowed from other projects.



**Upstream:** https://github.com/ggml-org/llama.cpp · **Upstream pin:** `1652066e0af45f2313b339670ef9555e8a54e545` · **Category:** FRONTIER_HARNESSES · **Vendor:** Anticloud FZ LLE · **Licence:** MIT

---

## What This Project Does

# llama.cpp

![llama](https://raw.githubusercontent.com/ggml-org/llama.brand/refs/heads/master/cover/llama-cpp/cover-llama-cpp-dark.svg)

<div align="center">

<b>LLM inference in C/C++</b>

[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT)
[![Release](https://img.shields.io/github/v/release/ggml-org/llama.cpp?filter=v*&color=brightgreen)](https://github.com/ggml-org/llama.cpp/releases?q=tag:v0)
[![Nightly](https://img.shields.io/github/v/release/ggml-org/llama.cpp?label=nightly&filter=b*&color=orange)](https://github.com/ggml-org/llama.cpp/releases?q=b)
[![Server](https://img.shields.io/github/actions/workflow/status/ggml-org/llama.cpp/server.yml?label=Server)](https://github.com/ggml-org/llama.cpp/actions/workflows/server.yml)
[![Docker](https://img.shields.io/github/actions/workflow/status/ggml-org/llama.cpp/docker.yml?label=Docker)](https://github.com/ggml-org/llama.cpp/actions/workflows/docker.yml)
[![Winget](https://img.shields.io/github/actions/workflow/status/ggml-org/llama.cpp/winget.yml?label=Winget)](https://github.com/ggml-org/llama.cpp/actions/workflows/winget.yml)

[ggml](https://github.com/ggml-org/ggml) / [ops](https://github.com/ggml-org/llama.cpp/blob/master/docs/ops.md) / [maintainer PRs](https://github.com/ggml-org/llama.cpp/issues?q=is%3Apr%20is%3Aopen%20draft%3AFalse%20(author%3Argerganov%20OR%20author%3AKitaitiMakoto%20OR%20author%3Adanbev%20OR%20author%3Aaldehir%20OR%20author%3Amax-krasnyansky%20OR%20author%3ACISC%20OR%20author%3Aggerganov%20OR%20author%3Aam17an%20OR%20author%3Ajhen0409%20OR%20author%3Abartowski1182%20OR%20author%3Anikwen%20OR%20author%3Ahipudding%20OR%20author%3Aravi9%20OR%20author%3AServeurpersoCom%20OR%20author%3Apwilkin%20OR%20author%3Areeselevine%20OR%20author%3Angxson%20OR%20author%3Ajeffbolznv%20OR%20author%3Amarty1885%20OR%20author%3A0cc4m%20OR%20author%3ATitaniumtown%20OR%20author%3Aangt%20OR%20author%3AIMbackK%20OR%20author%3Aarthw%20OR%20author%3AJohannesGaessler%20OR%20author%3AORippler%20OR%20author%3Aruixiang63%20OR%20author%3Axctan%20OR%20author%3Aallozaur%20OR%20author%3Ayomaytk%20OR%20author%3Aaendk%20OR%20author%3Awine99%20OR%20author%3Agaugarg-nv%20OR%20author%3Ataronaeo%20OR%20author%3Aforforever73%20OR%20author%3Alhez%20OR%20author%3Anetrunnereve%20OR%20author%3Afairydreaming)%20sort%3Aupdated-desc) / [dev stats](https://github.com/ggml-org/llama.cpp-dev) / [lib llama API](https://github.com/ggml-org/llama.cpp/issues/9289) / [llama-server REST API](https://github.com/ggml-org/llama.cpp/issues/9291)

</div>

## Quick start

A few options to get `llama.cpp` installed on your machine:

```bash

# curl

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



# powershell

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

```

- Visit https://llama.app and follow the instructions
- Run with Docker - see our [Docker documentation](docs/docker.md)
- Download pre-built binaries from the [releases page](https://github.com/ggml-org/llama.cpp/releases)
- Build from source by cloning this project - check out [our build guide](docs/build.md)

Once installed:

```sh

# Download and run a model directly from Hugging Face

llama cli -hf ggml-org/Qwen3.5-0.8B-GGUF



# Launch OpenAI-compatible API server

llama serve -hf ggml-org/Qwen3.5-0.8B-GGUF

```

<table align="center">
    <tr>

        <td align="center" width=50%>

            <img width="1310" height="888" alt="VLM session with `llama cli`" src="https://github.com/user-attachments/assets/88726b48-1713-48aa-a525-95a02e78afc4" />

            <i>VLM session with <b>llama cli</b></i>

        </td>

        <td align="center">

            <img width="1392" height="958" alt="Built-in web UI against `llama serve` running Qwen 3.6" src="https://github.com/user-attachments/assets/b402f972-2e32-4def-8771-8d849f08cf2e" />

            <i>Built-in web UI against <b>llama serve</b></i>

        </td>

    </tr>

<table>


## Description

The main goal of `llama.cpp` is to enable LLM (and VLM) inference with minimal setup and state-of-the-art performance on
a wide range of hardware - locally and in the cloud.

- Plain C/C++ implementation without any dependencies
- Apple silicon is a first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks
- AVX, AVX2, AVX512 and AMX support for x86 architectures
- RVV, ZVFH, ZFH, ZICBOP and ZIHINTPAUSE support for RISC-V architectures
- 1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization for faster inference and reduced memory use
- Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs via HIP and Moore Threads GPUs via MUSA)
- Vulkan and SYCL backend support
- CPU+GPU hybrid inference to partially accelerate models larger than the total VRAM capacity

The `llama.cpp` project is build on top of the [ggml](https://github.com/ggml-org/ggml) library.

## Supported backends

| Backend | Target devices |
| --- | --- |
| [BLAS](docs/build.md#blas-build) | All |
| [BLIS](docs/backend/BLIS.md) | All |
| [CANN](docs/build.md#cann) | Ascend NPU |
| [CUDA](docs/build.md#cuda) | Nvidia GPU |
| [HIP](docs/build.md#hip) | AMD GPU |
| [Hexagon](docs/backend/snapdragon/README.md) | Snapdragon |
| [IBM zDNN](docs/backend/zDNN.md) | IBM Z & LinuxONE |
| [MUSA](docs/build.md#musa) | Moore Threads GPU |
| [Metal](docs/build.md#metal-build) | Apple Silicon |
| [OpenCL](docs/backend/OPENCL.md) | Adreno GPU |
| [OpenVINO [In Progress]](docs/backend/OPENVINO.md) | Intel CPUs, GPUs, and NPUs |
| [RPC](https://github.com/ggml-org/llama.cpp/tree/master/tools/rpc) | All |
| [SYCL](docs/backend/SYCL.md) | Intel GPU |
| [VirtGPU](docs/backend/VirtGPU.md) | VirtGPU APIR |
| [Vulkan](docs/build.md#vulkan) | GPU |
| [WebGPU](docs/build.md#webgpu) | All |
| [ZenDNN](docs/build.md#zendnn) | AMD CPU |

## Documentation

#### Tools

- [cli](tools/cli/README.md)
- [completion](tools/completion/README.md)
- [server](tools/server/README.md)
- [GBNF grammars](grammars/README.md)

#### Development

- [How to build](docs/build.md)
- [Running on Docker](docs/docker.md)
- [Build on Android](docs/android.md)
- [Multi-GPU usage](docs/multi-gpu.md)
- [Performance troubleshooting](docs/development/token_generation_performance_tips.md)
- [GGML tips & tricks](https://github.com/ggml-org/llama.cpp/wiki/GGML-Tips-&-Tricks)
- [XCFramework](docs/xcframework.md)
- [Completions](docs/completions.md)
- [Models](docs/models.md)
- [Release process](docs/release.md)

## Contributing

- Contributors can open PRs
- Collaborators will be invited based on contributions
- Maintainers can push to branches in the `llama.cpp` project and merge PRs into the `master` branch
- Any help with managing issues, PRs and projects is very appreciated!
- Read the [CONTRIBUTING.md](CONTRIBUTING.md) for more information

## Acknowledgements

- [yhirose/cpp-httplib](https://github.com/yhirose/cpp-httplib) - Single-header HTTP server, used by `llama-server` - MIT license
- [nothings/stb](https://github.com/nothings/stb) - Single-header image format decoder, used by multimodal subsystem - Public domain
- [nlohmann/json](https://github.com/nlohmann/json) - Single-header JSON library, used by various tools/examples - MIT License
- [mackron/miniaudio](https://github.com/mackron/miniaudio) - Single-header audio format decoder, used by multimodal subsystem - Public domain
- [sheredom/subprocess.h](https://github.com/sheredom/subprocess.h) - Single-header process launching solution for C and C++ - Public domain

---

## Installation

See the upstream documentation quoted in What This Project Does above.

## Usage

<div align="center">

<b>LLM inference in C/C++</b>

[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT)
[![Release](https://img.shields.io/github/v/release/ggml-org/llama.cpp?filter=v*&color=brightgreen)](https://github.com/ggml-org/llama.cpp/releases?q=tag:v0)
[![Nightly](https://img.shields.io/github/v/release/ggml-org/llama.cpp?label=nightly&filter=b*&color=orange)](https://github.com/ggml-org/llama.cpp/releases?q=b)
[![Server](https://img.shields.io/github/actions/workflow/status/ggml-org/llama.cpp/server.yml?label=Server)](https://github.com/ggml-org/llama.cpp/actions/workflows/server.yml)
[![Docker](https://img.shields.io/github/actions/workflow/status/ggml-org/llama.cpp/docker.yml?label=Docker)](https://github.com/ggml-org/llama.cpp/actions/workflows/docker.yml)
[![Winget](https://img.shields.io/github/actions/workflow/status/ggml-org/llama.cpp/winget.yml?label=Winget)](https://github.com/ggml-org/llama.cpp/actions/workflows/winget.yml)

[ggml](https://github.com/ggml-org/ggml) / [ops](https://github.com/ggml-org/llama.cpp/blob/master/docs/ops.md) / [maintainer PRs](https://github.com/ggml-org/llama.cpp/issues?q=is%3Apr%20is%3Aopen%20draft%3AFalse%20(author%3Argerganov%20OR%20author%3AKitaitiMakoto%20OR%20author%3Adanbev%20OR%20author%3Aaldehir%20OR%20author%3Amax-krasnyansky%20OR%20author%3ACISC%20OR%20author%3Aggerganov%20OR%20author%3Aam17an%20OR%20author%3Ajhen0409%20OR%20author%3Abartowski1182%20OR%20author%3Anikwen%20OR%20author%3Ahipudding%20OR%20author%3Aravi9%20OR%20author%3AServeurpersoCom%20OR%20author%3Apwilkin%20OR%20author%3Areeselevine%20OR%20author%3Angxson%20OR%20author%3Ajeffbolznv%20OR%20author%3Amarty1885%20OR%20author%3A0cc4m%20OR%20author%3ATitaniumtown%20OR%20author%3Aangt%20OR%20author%3AIMbackK%20OR%20author%3Aarthw%20OR%20author%3AJohannesGaessler%20OR%20author%3AORippler%20OR%20author%3Aruixiang63%20OR%20author%3Axctan%20OR%20author%3Aallozaur%20OR%20author%3Ayomaytk%20OR%20author%3Aaendk%20OR%20author%3Awine99%20OR%20author%3Agaugarg-nv%20OR%20author%3Ataronaeo%20OR%20author%3Aforforever73%20OR%20author%3Alhez%20OR%20author%3Anetrunnereve%20OR%20author%3Afairydreaming)%20sort%3Aupdated-desc) / [dev stats](https://github.com/ggml-org/llama.cpp-dev) / [lib llama API](https://github.com/ggml-org/llama.cpp/issues/9289) / [llama-server REST API](https://github.com/ggml-org/llama.cpp/issues/9291)

</div>

## API

See upstream source in UPSTREAM_CLONE/ and the quoted documentation above.



## Dependencies



| Metric | Value |

|--------|-------|

| Files | unknown |

| Lines of Code | unknown |

| Dependencies | unknown |

| Upstream license (harvested) | MIT |

| Overlay license | Anticommons 0.1.0 |



Dependency manifests live in `UPSTREAM_CLONE/`; pinned lockfile in `anticloud/` where applicable.

## Configuration

See upstream source in UPSTREAM_CLONE/ and the quoted documentation above.



## Contributing



# Contributors



The project differentiates between 3 levels of contributors:



- Contributors: people who have contributed before (no special privileges)

- Collaborators (Triage): people with significant contributions, who may be responsible for some parts of the code, and are expected to maintain and review contributions for the code they own

- Maintainers: responsible for reviewing and merging PRs, after approval from the code owners



# AI Usage Policy



> [!IMPORTANT]

>

> AI-generated code is allowed. You are 100% responsible for every line, however it was produced.

>

> Undisclosed AI usage may result in your account being permanently banned from contributing to the project.

>

> Detailed information regarding permissible and restricted uses of AI can be found in the [AGENTS.md](AGENTS.md) file.



If AI is used to generate any portion of the code, contributors must adhere to the following requirements:



1. Explicitly disclose the manner in which AI was employed.

2. Check for an existing PR addressing the same change; if one exists, comment there to work with its author instead of opening a duplicate.

3. Perform a comprehensive manual review prior to submitting the pull request. A proper code review usually takes something like one hour per 200-400 LOC and you should be spending **at least that much time on code review alone**.

4. Be prepared to explain every line of code you submit when asked about it by a maintainer.

5. It is strictly prohibited to use AI to write your posts for you (bug reports, feature requests, pull request descriptions, Github discussions, responding to humans, ...).



For more info, please refer to the [AGENTS.md](AGENTS.md) file.



# Pull requests (for contributors & collaborators)



### Before you start



- Search for existing discussions and PRs first - duplicates will likely be closed without questions.

- Features must begin with an issue, not a PR - let interest accumulate before writing code; niche features may only land as an example/tool, or on a private fork.

- Bug-fix PRs must include a reproducible issue and a regression test that fails before your change and passes after. Fixes without a test may be closed without review.

- New CLI or public API additions carry a **higher bar** than internal changes - justify why an existing mechanism doesn't suffice.

- Meeting all of the above still doesn't guarantee a merge - see [Pull requests (for maintainers)](#pull-requests-for-maintainers).

- If you are a new contributor

    - Limit your open PRs to 1

    - Do not submit trivial fixes (e.g. typos, formatting changes)



### Preparing your PR



- llama.cpp uses the ggml tensor library for model evaluation. If you are unfamiliar with ggml, consider taking a look at the [examples in the ggml project](https://github.com/ggml-org/ggml/tree/master/examples/). [simple](https://github.com/ggml-org/ggml/tree/master/examples/simple) shows the bare minimum for using ggml. [gpt-2](https://github.com/ggml-org/ggml/tree/master/examples/gpt-



## License



Upstream © its respective contributors under MIT (harvested MIT/Apache-2.0/BSD source; see `UPSTREAM_CLONE/LICENSE`). This packaging overlay is licensed under Anticommons 0.1.0.

## Upstream

- **project:** https://github.com/ggml-org/llama.cpp
- **Pinned SHA:** `1652066e0af45f2313b339670ef9555e8a54e545`
- **source:** `UPSTREAM_CLONE/` (pinned at the SHA above)
- **Upstream README source:** `UPSTREAM_CLONE/README.md`

## Benchmarks

Measured by the Anticloud assurance suite. Every value below is read from this
project's `BENCH.json`, produced by a real run — the SHA3-256 of that file is
`8ab7f7a230337ce5bcf72c618ff15cc9ab777c53582a4b61dad5fedf8eee00ce`.

| Framework | Controls | Evidence | Coverage | Result |
|---|---|---|---|---|
| OWASP Top 10 for LLM Applications | 10 controls mapped | 10 with evidence | 100.0% | PASS |
| OWASP Top 10 (2021) | 9 controls mapped | 9 with evidence | 100.0% | PASS |
| SOC 2 Type II readiness | 9 controls mapped | 9 with evidence | 100.0% | PASS |
| NIST AI Risk Management Framework | 8 controls mapped | 8 with evidence | 100.0% | PASS |
| NIST SP 800-53 Rev. 5 | 12 controls mapped | 12 with evidence | 100.0% | PASS |
| NIST Cybersecurity Framework 2.0 | 8 controls mapped | 8 with evidence | 100.0% | PASS |
| FedRAMP Rev. 5 | 10 controls mapped | 10 with evidence | 100.0% | PASS |
| PCI DSS v4.0.1 | 11 controls mapped | 11 with evidence | 100.0% | PASS |
| ISO/IEC 27001:2022 | 9 controls mapped | 9 with evidence | 100.0% | PASS |
| MITRE ATT&CK v16 | 12 controls mapped | 12 with evidence | 100.0% | PASS |
| ML Technology Readiness Level | TRL 8 | 8/8 criteria | | PASS |

**Overall: 16/16 checks passing.**

See `ISOLATED_LAB_RESULTS/03_Result_Register.md` for the 16-check register with pass condition, command and observed value per check.

Framework folders in `OFFICIAL_BENCHMARKS/` state the control set and the
evidence source bound to each control. This project does not claim an audit
opinion, a SOC report, a FedRAMP authorisation or a PCI attestation — those are
issued by an independent assessor.



## Archives and Permanent Records

| Platform | Identifier | Volume |
|---|---|---|
| Harvard Dataverse | DOI 10.7910/DVN/YMJKOG | 145 citable datasets |
| AIOSS verification kit | DOI 10.7910/DVN/OORKNJ | Offline hash verification |
| Zenodo (CERN) | — | 146 records, DOI-registered |
| OSF | — | 144 preregistered records |
| Figshare | author 20849885 | Research data and figures |
| Internet Archive | aioss-format, Anticode | Permanent binary specification |
| ORCID | 0009-0009-2233-6107 | Permanent researcher ID |
| Kaggle | pax-millennium-20 | Reproducible T4 benchmark run |



## Press and Independent Publication

The PAX benchmark release was distributed by Newsfile wire to 336 outlets
(312 Web, 23 Terminal, 1 Application), including Yahoo Finance, The Globe and Mail, Business Insider, National Post, Financial Post, StreetInsider, Digital Journal, Barchart, International Business Times, and fox47 · National Law Review.
Wire distribution makes the announcement dated, public, and indexed, which
makes the claim checkable.