AIencoder/llama-cpp-wheels

🏭 llama-cpp-python Prebuilt Wheels

The most complete collection of prebuilt llama-cpp-python wheels for manylinux x86_64.

Stop compiling. Start inferencing.

pip install https://huggingface.co/datasets/AIencoder/llama-cpp-wheels/resolve/main/llama_cpp_python-0.3.16+openblas_avx2_fma_f16c-cp311-cp311-manylinux_2_31_x86_64.whl

📊 What's Inside

	Count
Total Wheels	3,794+
Versions	0.3.0 — 0.3.16 (17 versions)
Python	3.8, 3.9, 3.10, 3.11, 3.12, 3.13, 3.14
Platform	manylinux_2_31_x86_64
Backends	8
CPU Profiles	13+ flag combinations

⚡ Backends

Backend	Tag	Description
OpenBLAS	openblas	CPU BLAS acceleration — best general-purpose choice
Intel MKL	mkl	Intel Math Kernel Library — fastest on Intel CPUs
Basic	basic	No BLAS — maximum compatibility, no extra dependencies
Vulkan	vulkan	Universal GPU acceleration — works on NVIDIA, AMD, Intel
CLBlast	clblast	OpenCL GPU acceleration
SYCL	sycl	Intel GPU acceleration (Data Center, Arc, iGPU)
OpenCL	opencl	Generic OpenCL GPU backend
RPC	rpc	Distributed inference over network

🖥️ CPU Optimization Profiles

Wheels are built with specific CPU instruction sets enabled. Pick the one that matches your hardware:

CPU Tag	Instructions	Best For
basic	None	Any x86-64 CPU (maximum compatibility)
avx	AVX	Sandy Bridge+ (2011)
avx_f16c	AVX + F16C	Ivy Bridge+ (2012)
avx2_fma_f16c	AVX2 + FMA + F16C	Haswell+ (2013) — most common
avx2_fma_f16c_avxvnni	AVX2 + FMA + F16C + AVX-VNNI	Alder Lake+ (2021)
avx512_fma_f16c	AVX-512 + FMA + F16C	Skylake-X+ (2017)
avx512_fma_f16c_vnni	+ AVX512-VNNI	Cascade Lake+ (2019)
avx512_fma_f16c_vnni_vbmi	+ AVX512-VBMI	Ice Lake+ (2019)
avx512_fma_f16c_vnni_vbmi_bf16_amx	+ BF16 + AMX	Sapphire Rapids+ (2023)

How to Pick the Right Wheel

Don't know your CPU? Start with avx2_fma_f16c — it works on any CPU from 2013 onwards (Intel Haswell, AMD Ryzen, and newer).

Want maximum compatibility? Use basic — works on literally any x86-64 CPU.

Have a server CPU? Check if it supports AVX-512:

grep -o 'avx[^ ]*\|fma\|f16c\|bmi2\|sse4_2' /proc/cpuinfo | sort -u

📦 Filename Format

All wheels follow the PEP 440 local version identifier standard:

llama_cpp_python-{version}+{backend}_{cpu_flags}-{python}-{python}-{platform}.whl

Examples:

llama_cpp_python-0.3.16+openblas_avx2_fma_f16c-cp311-cp311-manylinux_2_31_x86_64.whl
llama_cpp_python-0.3.16+vulkan-cp312-cp312-manylinux_2_31_x86_64.whl
llama_cpp_python-0.3.16+basic-cp310-cp310-manylinux_2_31_x86_64.whl

The local version label (+openblas_avx2_fma_f16c) encodes:

• Backend: openblas, mkl, basic, vulkan, clblast, sycl, opencl, rpc
• CPU flags (in order): avx, avx2, avx512, fma, f16c, vnni, vbmi, bf16, avxvnni, amx

🚀 Quick Start

CPU (OpenBLAS + AVX2 — recommended for most users)

sudo apt-get install libopenblas-dev

pip install https://huggingface.co/datasets/AIencoder/llama-cpp-wheels/resolve/main/llama_cpp_python-0.3.16+openblas_avx2_fma_f16c-cp311-cp311-manylinux_2_31_x86_64.whl

GPU (Vulkan — works on any GPU vendor)

sudo apt-get install libvulkan1

pip install https://huggingface.co/datasets/AIencoder/llama-cpp-wheels/resolve/main/llama_cpp_python-0.3.16+vulkan-cp311-cp311-manylinux_2_31_x86_64.whl

Basic (zero dependencies)

pip install https://huggingface.co/datasets/AIencoder/llama-cpp-wheels/resolve/main/llama_cpp_python-0.3.16+basic-cp311-cp311-manylinux_2_31_x86_64.whl

Example Usage

from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="Qwen/Qwen2.5-Coder-7B-Instruct-GGUF",
    filename="*q4_k_m.gguf",
    n_ctx=4096,
)

output = llm.create_chat_completion(
    messages=[{"role": "user", "content": "Write a Python hello world"}],
    max_tokens=256,
)
print(output["choices"][0]["message"]["content"])

🔧 Runtime Dependencies

Backend	Required Packages
OpenBLAS	libopenblas0 (runtime) or libopenblas-dev (build)
MKL	Intel oneAPI MKL
Vulkan	libvulkan1
CLBlast	libclblast1
OpenCL	ocl-icd-libopencl1
Basic	None
SYCL	Intel oneAPI DPC++ runtime
RPC	Network access to RPC server

🏭 How These Wheels Are Built

These wheels are built by the Ultimate Llama Wheel Factory — a distributed build system running entirely on free HuggingFace Spaces:

Component	Link
🏭 Dispatcher	wheel-factory-dispatcher
⚙️ Workers 1-4	wheel-factory-worker-1 ... 4
🔍 Auditor	wheel-factory-auditor

The factory uses explicit cmake flags matching llama.cpp's official CPU variant builds:

CMAKE_ARGS="-DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS -DGGML_AVX2=ON -DGGML_FMA=ON -DGGML_F16C=ON -DGGML_AVX=OFF -DGGML_AVX512=OFF -DGGML_NATIVE=OFF"

Every flag is set explicitly (no cmake defaults) to ensure reproducible, deterministic builds.

❓ FAQ

Q: Which wheel should I use? For most people: openblas_avx2_fma_f16c with your Python version. It's fast, works on 90%+ of modern CPUs, and only needs libopenblas.

Q: Can I use these on Ubuntu / Debian / Fedora / Arch? Yes — manylinux_2_31 wheels work on any Linux distro with glibc 2.31 or newer (Ubuntu 20.04+, Debian 11+, Fedora 34+, Arch).

Q: What about Windows / macOS / CUDA wheels? This repo focuses on manylinux x86_64. For other platforms, see:

• abetlen's official wheel index — CPU, CUDA 12.1-12.5, Metal
• jllllll's CUDA wheels — cuBLAS + AVX combos

Q: These wheels don't work on Alpine Linux. Alpine uses musl, not glibc. These are manylinux (glibc) wheels. Build from source or use musllinux wheels.

Q: I get "illegal instruction" errors. You're using a wheel with CPU flags your processor doesn't support. Try basic (no SIMD) or check your CPU flags with:

grep -o 'avx[^ ]*\|fma\|f16c' /proc/cpuinfo | sort -u

Q: Can I contribute more wheels? Yes! The factory source code is open. See the Dispatcher and Worker Spaces linked above.

📄 License

MIT — same as llama-cpp-python and llama.cpp.

🙏 Credits

• llama.cpp by Georgi Gerganov and the ggml community
• llama-cpp-python by Andrei Betlen
• Built with 🏭 by AIencoder