AIencoder/llama-cpp-wheels

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🏭 llama-cpp-python Mega-Factory Wheels

"Stop waiting for `pip` to compile. Just install and run."

The most complete collection of pre-built llama-cpp-python wheels in existence — 8,333 wheels across every platform, Python version, backend, and CPU optimization level.

No more cmake, gcc, or compilation hell. No more waiting 10 minutes for a build that might fail. Just find your wheel and pip install it directly.

🚀 Why These Wheels?

Standard wheels target the "lowest common denominator" to avoid crashes on old hardware. This collection goes further — the manylinux wheels are built using a massive Everything Preset targeting specific CPU instruction sets, maximizing your Tokens per Second (T/s).

• —Zero Dependencies: No cmake, gcc, or nvcc required on your target machine.
• —Every Platform: Linux (manylinux, aarch64, i686, RISC-V), Windows (amd64, 32-bit), macOS (Intel + Apple Silicon).
• —Server-Grade Power: Optimized builds for Sapphire Rapids, Ice Lake, Alder Lake, Haswell, and more.
• —Full Backend Support: OpenBLAS, MKL, Vulkan, CLBlast, OpenCL, RPC, and plain CPU builds.
• —Cutting Edge: Python 3.8 through experimental 3.14, plus PyPy pp38–pp310.
• —GPU Too: CUDA wheels (cu121–cu124) and macOS Metal wheels included.

📊 Collection Stats

The manylinux builds alone cover 3,600+ combinations across versions, backends, Python versions, and CPU profiles.

🚀 How to Install

Quick Install

Find your wheel filename (see naming convention below), then:

pip install "https://huggingface.co/datasets/AIencoder/llama-cpp-wheels/resolve/main/YOUR_WHEEL_NAME.whl"

Common Examples

# Linux x86_64, Python 3.11, OpenBLAS, Haswell CPU (most common Linux setup)
pip install "https://huggingface.co/datasets/AIencoder/llama-cpp-wheels/resolve/main/llama_cpp_python-0.3.18+openblas_haswell-cp311-cp311-manylinux_2_31_x86_64.whl"

# Linux x86_64, Python 3.12, Basic CPU (maximum compatibility)
pip install "https://huggingface.co/datasets/AIencoder/llama-cpp-wheels/resolve/main/llama_cpp_python-0.3.18+basic_basic-cp312-cp312-manylinux_2_31_x86_64.whl"

# Windows, Python 3.11
pip install "https://huggingface.co/datasets/AIencoder/llama-cpp-wheels/resolve/main/llama_cpp_python-0.3.18-cp311-cp311-win_amd64.whl"

# macOS Apple Silicon, Python 3.12
pip install "https://huggingface.co/datasets/AIencoder/llama-cpp-wheels/resolve/main/llama_cpp_python-0.3.18-cp312-cp312-macosx_11_0_arm64.whl"

# macOS Intel, Python 3.11
pip install "https://huggingface.co/datasets/AIencoder/llama-cpp-wheels/resolve/main/llama_cpp_python-0.3.18-cp311-cp311-macosx_10_9_x86_64.whl"

# Linux ARM64 (Raspberry Pi, AWS Graviton), Python 3.11
pip install "https://huggingface.co/datasets/AIencoder/llama-cpp-wheels/resolve/main/llama_cpp_python-0.3.18-cp311-cp311-linux_aarch64.whl"

📁 Wheel Naming Convention

manylinux wheels (custom-built)

llama_cpp_python-{version}+{backend}_{profile}-{pytag}-{pytag}-{platform}.whl

Versions covered: 0.3.0 through 0.3.18+

Backends:

CPU Profiles:

Python tags: cp38, cp39, cp310, cp311, cp312, cp313, cp314, pp38, pp39, pp310

Platform: manylinux_2_31_x86_64 (glibc 2.31+, compatible with Ubuntu 20.04+, Debian 11+)

Windows / macOS / Linux ARM wheels (from abetlen)

llama_cpp_python-{version}-{pytag}-{pytag}-{platform}.whl

These are the official pre-built wheels from the upstream maintainer, covering versions 0.2.82 through 0.3.18+.

🔍 How to Find Your Wheel

1. 1.Identify your Python version: python --version → e.g. 3.11 → tag cp311
2. 2.Identify your platform:
3. 3.Linux x86\64 → `manylinux231x86_64`
4. 4.Windows 64-bit → win_amd64
5. 5.macOS Apple Silicon → macosx_11_0_arm64
6. 6.macOS Intel → macosx_10_9_x86_64
7. 7.Pick a backend (manylinux only): openblas for most use cases
8. 8.Pick a CPU profile (manylinux only): haswell works on virtually all modern CPUs
9. 9.Browse the files in this repo or construct the filename directly

🏗️ Sources & Credits

manylinux Wheels — Built by AIencoder

The 4,940 manylinux x86\_64 wheels were built by a distributed 4-worker HuggingFace Space factory system (AIencoder/wheel-factory-*) — a custom-built automated pipeline covering every possible llama.cpp cmake option on manylinux:

• —Every backend: OpenBLAS, MKL, Basic, Vulkan, CLBlast, OpenCL, RPC
• —Every CPU hardware profile from baseline x86-64 up to Sapphire Rapids AMX
• —Python 3.8 through 3.14
• —llama-cpp-python versions 0.3.0 through 0.3.18+

Windows / macOS / Linux ARM Wheels — abetlen

The remaining 3,393 wheels (Windows, macOS, Linux aarch64/i686/riscv64, PyPy) were sourced from the official releases by Andrei Betlen ([@abetlen](https://github.com/abetlen)), the original author and maintainer of llama-cpp-python. These include:

• —CPU wheels for all platforms via https://abetlen.github.io/llama-cpp-python/whl/cpu/
• —Metal wheels for macOS GPU acceleration
• —CUDA wheels (cu121–cu124) for Windows and Linux

All credit for the underlying library goes to Georgi Gerganov ([@ggerganov](https://github.com/ggerganov)) and the llama.cpp team, and to Andrei Betlen for the Python bindings.

📝 Notes

• —All wheels are MIT licensed (same as llama-cpp-python upstream)
• —manylinux wheels require glibc 2.31+ (Ubuntu 20.04+, Debian 11+)
• —manylinux and linux_x86_64 are not the same thing — manylinux wheels have broad distro compatibility, plain linux wheels do not
• —CUDA wheels require the matching CUDA toolkit to be installed
• —Metal wheels require macOS 11.0+ and an Apple Silicon or AMD GPU
• —This collection is updated periodically as new versions are released