AIencoder/llama-cpp-wheels

🏭 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.

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🚀 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.

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📊 Collection Stats

Platform	Wheels
🐧 Linux x86_64 (manylinux)	4,940
🍎 macOS Intel (x86_64)	1,040
🪟 Windows (amd64)	1,010
🪟 Windows (32-bit)	634
🍎 macOS Apple Silicon (arm64)	289
🐧 Linux i686	214
🐧 Linux aarch64	120
🐧 Linux x86_64 (plain)	81
🐧 Linux RISC-V	5
Total	8,333

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

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🚀 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"

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📁 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:

Backend	Description
openblas	OpenBLAS BLAS acceleration — best general-purpose CPU performance
mkl	Intel MKL acceleration — best on Intel CPUs
basic	No BLAS, maximum compatibility
vulkan	Vulkan GPU backend
clblast	CLBlast OpenCL GPU backend
opencl	Generic OpenCL GPU backend
rpc	Distributed inference over network

CPU Profiles:

Profile	Instruction Sets	Era	Notes
basic	x86-64 baseline	Any	Maximum compatibility
sse42	SSE 4.2	2008+	Nehalem
sandybridge	AVX	2011+
ivybridge	AVX + F16C	2012+
haswell	AVX2 + FMA + BMI2	2013+	Most common
skylakex	AVX-512	2017+
icelake	AVX-512 + VNNI + VBMI	2019+
alderlake	AVX-VNNI	2021+
sapphirerapids	AVX-512 BF16 + AMX	2023+	Highest performance

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+.

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🔍 How to Find Your Wheel

1. Identify your Python version: python --version → e.g. 3.11 → tag cp311
2. Identify your platform:
   • Linux x86_64 → manylinux_2_31_x86_64
   • Windows 64-bit → win_amd64
   • macOS Apple Silicon → macosx_11_0_arm64
   • macOS Intel → macosx_10_9_x86_64
3. Pick a backend (manylinux only): openblas for most use cases
4. Pick a CPU profile (manylinux only): haswell works on virtually all modern CPUs
5. Browse the files in this repo or construct the filename directly

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🏗️ 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), 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) and the llama.cpp team, and to Andrei Betlen for the Python bindings.

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📝 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