AIencoder/llama-cpp-wheels

llama-cpp-python Wheels — the complete collection

Prebuilt llama-cpp-python wheels, gathered to be the single most complete place to grab one: 10,000+ wheels covering Linux, Windows, and macOS, every published version from 0.1.x to 0.3.x, Python 3.7 – 3.14, and every backend this project can build — from plain CPU up through AVX-512/AMX, OpenBLAS, MKL, Vulkan, OpenCL, SYCL, CLBlast, Metal, and CUDA 12.1 → 12.9.

It combines two things:

1. A full mirror of the official upstream wheels — everything abetlen publishes (cpu, metal, cu121–cu124) across every version and OS.
2. Thousands of builds upstream never shipped — produced here by an automated GitHub Actions "wheel factory": newer CUDA versions (12.6 / 12.8 / 12.9), every CPU instruction-set tier, and the OpenBLAS / MKL / Vulkan / OpenCL / SYCL / CLBlast / RPC backends, for the latest releases.

These are self-contained. CUDA wheels bundle the CUDA runtime, so you only need an NVIDIA driver — not the full CUDA toolkit — to use them.

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Quick start

Every wheel is a direct download. Point pip at its URL:

pip install "https://huggingface.co/datasets/AIencoder/llama-cpp-wheels/resolve/main/linux/x86_64/cuda12.4/0.3.19/llama_cpp_python-0.3.19+cu124-cp311-cp311-manylinux_2_31_x86_64.whl"

Browse the folders (below) to find the one matching your OS, CPU, GPU, Python, and version.

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How the collection is organised

Wheels are nested os / arch / backend / version /:

linux/
  x86_64/
    cpu/                         ← plain CPU, no special instructions (max compatibility)
    cpu-avx2-fma-f16c/           ← CPU, AVX2 tier (most modern x86 CPUs)
    cpu-avx512-fma-f16c-vnni-vbmi-bf16-amx/   ← newest Intel/AMD server CPUs
    openblas-avx2-fma-f16c/
    mkl-avx2-fma-f16c/
    vulkan/  opencl/  clblast/  sycl/  rpc/
    cuda12.4/  cuda12.6/  cuda12.8/  cuda12.9/
      0.3.19/
        llama_cpp_python-0.3.19+cu124-cp311-cp311-manylinux_2_31_x86_64.whl
        …one per Python version…
    aarch64/ …     i686/ …
windows/
  x86_64/
    cpu/  cuda12.4/  cuda12.6/  cuda12.8/   (+ upstream-mirrored variants)
      0.3.19/ …
macos/
  arm64/   metal/ …   (Apple Silicon)
  x86_64/  metal/ …   (Intel Macs)

Why this order: os is the hardest constraint (and a clean 3-way split at the root); arch keeps arm64/aarch64/i686 wheels from being mixed in with x86_64; backend is the capability you actually choose; version sits at the leaf, which then holds just the per-Python builds for that exact combination. Every leaf folder stays tiny, which also keeps the repo under HuggingFace's hard limit of 10,000 files per directory.

(Some early wheels may still sit in the repository root during migration — both are valid; use the file browser or search.)

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Choosing the right wheel

You need to match four things. Work down the folder tree:

1. OS + architecture

linux (manylinux/musllinux), windows, or macos; then your CPU architecture — x86_64 (most desktops/servers, incl. Windows and Intel Macs), arm64 (Apple Silicon), or aarch64 (ARM Linux).

2. Python version

The cpXY tag in the filename is your Python: cp311 = CPython 3.11. Check with python --version. Pick the wheel whose cpXY matches exactly.

3. Backend — the important choice

CPU (no GPU): pick the highest instruction tier your CPU supports. Going too high causes an Illegal instruction crash; too low just runs slower.

Folder	Needs	Who it's for
cpu	nothing	oldest CPUs / max compatibility / VMs
cpu-avx , cpu-avx-f16c	AVX	pre-2013 CPUs
cpu-avx2-fma-f16c	AVX2	most modern desktops/laptops
cpu-avx2-fma-f16c-avxvnni	AVX2 + VNNI	Alder Lake+ consumer Intel
cpu-avx512-fma-f16c	AVX-512	HEDT / server
cpu-avx512-…-vnni-vbmi , …-bf16-amx	AVX-512 VNNI/VBMI/BF16/AMX	Sapphire Rapids & newer servers

Check your CPU: Linux lscpu | grep -o 'avx[^ ]*' · or pip install py-cpuinfo && python -c "import cpuinfo,pprint; pprint.pp(cpuinfo.get_cpu_info()['flags'])" on any OS.

NVIDIA GPU: use a cudaXX.Y folder. Run nvidia-smi — the top-right "CUDA Version: 12.x" is the maximum your driver supports. Pick a folder with that minor version or lower (e.g. driver shows 12.6 → cuda12.4 or cuda12.6 both work; CUDA 12.x is forward-compatible within the 12 series). You do not need the CUDA toolkit installed — only the driver — because the runtime is bundled.

Apple GPU: metal (Apple Silicon and Intel Macs with a Metal GPU).

Other accelerators / BLAS:

Folder	What it is
openblas-*	OpenBLAS-accelerated CPU BLAS (faster prompt processing on CPU)
mkl-*	Intel MKL BLAS (fastest on Intel CPUs)
vulkan	cross-vendor GPU (AMD / Intel / NVIDIA via Vulkan)
opencl / clblast	OpenCL GPU acceleration (incl. older/mobile GPUs)
sycl	Intel GPUs (oneAPI / SYCL)
rpc	distributed inference across machines

4. Version

0.3.19 is the newest release built here; older ones are kept for reproducibility. Newer is usually better unless you're pinning for compatibility.

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Filename anatomy

llama_cpp_python-0.3.19+cu124-cp311-cp311-manylinux_2_31_x86_64.whl
                 │      │     │     │     └── platform (manylinux… / win_amd64 / macosx…)
                 │      │     └─────┴──────── Python (cp311 = CPython 3.11)
                 │      └── backend tag  (+cu124, +basic_avx2_fma_f16c, +openblas_…, +vulkan; absent = base build)
                 └── llama-cpp-python version

The folder name normalises that backend tag into something readable (cu124 → cuda12.4, basic_avx512_fma_f16c_vnni_vbmi → cpu-avx512-fma-f16c-vnni-vbmi).

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More install examples

# Linux, modern CPU, no GPU (AVX2):
pip install "https://huggingface.co/datasets/AIencoder/llama-cpp-wheels/resolve/main/linux/x86_64/cpu-avx2-fma-f16c/0.3.19/llama_cpp_python-0.3.19+basic_avx2_fma_f16c-cp311-cp311-manylinux_2_31_x86_64.whl"

# Windows, NVIDIA GPU, CUDA 12.6, Python 3.11:
pip install "https://huggingface.co/datasets/AIencoder/llama-cpp-wheels/resolve/main/windows/x86_64/cuda12.6/0.3.19/llama_cpp_python-0.3.19+cu126-cp311-cp311-win_amd64.whl"

# macOS Apple Silicon, Metal, Python 3.12:
pip install "https://huggingface.co/datasets/AIencoder/llama-cpp-wheels/resolve/main/macos/arm64/metal/0.3.4/llama_cpp_python-0.3.4-cp312-cp312-macosx_11_0_arm64.whl"

Programmatic download

from huggingface_hub import hf_hub_download
path = hf_hub_download(
    repo_id="AIencoder/llama-cpp-wheels", repo_type="dataset",
    filename="linux/x86_64/cuda12.4/0.3.19/llama_cpp_python-0.3.19+cu124-cp311-cp311-manylinux_2_31_x86_64.whl",
)
# then: pip install <path>

List everything with HfApi().list_repo_files("AIencoder/llama-cpp-wheels", repo_type="dataset").

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Coverage

	Linux (x86_64, plus some aarch64/i686)	Windows (x86_64)	macOS (arm64 + x86_64)
CPU	plain + every AVX/AVX2/AVX-512 tier up to VNNI·VBMI·BF16·AMX	upstream mirror	upstream mirror
BLAS	OpenBLAS, MKL (every tier)	upstream mirror	—
GPU	CUDA 12.4 / 12.6 / 12.8 / 12.9, Vulkan, OpenCL, CLBlast, SYCL	CUDA 12.4 / 12.6 / 12.8	Metal
Other	RPC	—	—

• Python: cp38 – cp314 for factory builds; the upstream mirror goes back to cp37 / PyPy on old versions.
• Versions: factory builds target 0.3.16 – 0.3.19; the mirror covers the full history (0.1.x → 0.3.x).
• Totals: 10,000+ wheels and growing. The upstream mirror targets all ~6,800 official wheels; the factory adds several thousand more that upstream never produced.

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How these wheels are built

The non-mirror wheels are compiled automatically by a GitHub Actions "factory":

• No GPU required to build — nvcc cross-compiles the CUDA kernels on ordinary CPU runners.
• Built from the official abetlen/llama-cpp-python source at the exact tagged version.
• Self-contained: Linux wheels are auditwheel-repaired; Windows CUDA wheels bundle the CUDA runtime DLLs (cudart, cublas, cublasLt) next to llama.dll. You need only the GPU driver.
• Each wheel is installed and structurally verified (dependency tree checked) before upload.
• The build queue is derived by diffing this dataset against the target matrix, so the collection fills in and stays current on its own.

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Caveats & troubleshooting

• Could not find module 'llama.dll' (or one of its dependencies) (Windows CUDA): you don't have an NVIDIA driver installed, or it's too old for the CUDA minor version you picked. Install/update the driver, or drop to a lower cudaXX.Y folder.
• Illegal instruction on import (CPU): the wheel's instruction tier is higher than your CPU supports. Use a lower cpu-… folder (when unsure, plain cpu).
• CUDA wheels are large (~0.6 – 1.3 GB): expected — they bundle the runtime and the fat multi-arch CUDA kernels, which is exactly what lets them run without a separate CUDA install.
• GPU wheels can't be import-tested without a GPU — that's normal; the build verifies structure, not a live GPU load.
• Community builds, provided as-is under the same MIT license as llama-cpp-python.

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Maintained by AIencoder. Wheels are builds of abetlen/llama-cpp-python; all credit for llama-cpp-python and llama.cpp to their authors.