LLAMA_CPP
Governed Anticloud packaging of the upstream project
LLAMA_CPPin 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
LLM inference in C/C++
ggml / ops / maintainer PRs / dev stats / lib llama API / llama-server REST API
Quick start
A few options to get llama.cpp installed on your machine:
# 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
- Download pre-built binaries from the releases page
- Build from source by cloning this project - check out our build guide
Once installed:
# 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
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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 library.
Supported backends
| Backend | Target devices |
|---|---|
| BLAS | All |
| BLIS | All |
| CANN | Ascend NPU |
| CUDA | Nvidia GPU |
| HIP | AMD GPU |
| Hexagon | Snapdragon |
| IBM zDNN | IBM Z & LinuxONE |
| MUSA | Moore Threads GPU |
| Metal | Apple Silicon |
| OpenCL | Adreno GPU |
| OpenVINO [In Progress] | Intel CPUs, GPUs, and NPUs |
| RPC | All |
| SYCL | Intel GPU |
| VirtGPU | VirtGPU APIR |
| Vulkan | GPU |
| WebGPU | All |
| ZenDNN | AMD CPU |
Documentation
Tools
Development
- How to build
- Running on Docker
- Build on Android
- Multi-GPU usage
- Performance troubleshooting
- GGML tips & tricks
- XCFramework
- Completions
- Models
- Release process
Contributing
- Contributors can open PRs
- Collaborators will be invited based on contributions
- Maintainers can push to branches in the
llama.cppproject and merge PRs into themasterbranch - Any help with managing issues, PRs and projects is very appreciated!
- Read the CONTRIBUTING.md for more information
Acknowledgements
- yhirose/cpp-httplib - Single-header HTTP server, used by
llama-server- MIT license - nothings/stb - Single-header image format decoder, used by multimodal subsystem - Public domain
- nlohmann/json - Single-header JSON library, used by various tools/examples - MIT License
- mackron/miniaudio - Single-header audio format decoder, used by multimodal subsystem - Public domain
- 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
LLM inference in C/C++
ggml / ops / maintainer PRs / dev stats / lib llama API / llama-server REST API
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
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 file.
If AI is used to generate any portion of the code, contributors must adhere to the following requirements:
- Explicitly disclose the manner in which AI was employed.
- Check for an existing PR addressing the same change; if one exists, comment there to work with its author instead of opening a duplicate.
- 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.
- Be prepared to explain every line of code you submit when asked about it by a maintainer.
- 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 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).
- 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. 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.