| --- |
| license: mit |
| task_categories: |
| - text-generation |
| language: |
| - en |
| tags: |
| - code |
| - llama-cpp |
| - llama-cpp-python |
| - wheels |
| - pre-built |
| - binary |
| - linux |
| - windows |
| - macos |
| pretty_name: llama-cpp-python Pre-Built Wheels |
| size_categories: |
| - 1K<n<10K |
| --- |
| If you like this please consider liking and donating (https://buymeacoffee.com/aiencoder) |
|
|
| # 🏭 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 |
|
|
| | 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. |
| |
| --- |
| |
| ## 🚀 How to Install |
| |
| ### Quick Install |
| |
| Find your wheel filename (see naming convention below), then: |
| |
| ```bash |
| pip install "https://huggingface.co/datasets/AIencoder/llama-cpp-wheels/resolve/main/YOUR_WHEEL_NAME.whl" |
| ``` |
| |
| ### Common Examples |
| |
| ```bash |
| # 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:** |
| |
| | 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+`. |
| |
| --- |
| |
| ## 🔍 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 |
|
|
| --- |
|
|
| ## 🏗️ 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](https://github.com/ggml-org/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 |
| |
