README.md

---
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
---

# 🏭 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