# Install on Linux ARM64

Install Kwker on Linux ARM64 servers (AWS Graviton, Ampere, NVIDIA Grace, Neoverse) - the Python wheels, the C library, and what is experimental there.

Web page: https://kwker.io/docs/?p=install-linux-arm64 - every Kwker documentation page for AI assistants: https://kwker.io/llms.txt

Kwker runs on 64-bit ARM Linux: AWS Graviton, Ampere Altra, NVIDIA Grace and other Neoverse servers. In 0.1.0 this
platform is experimental: every package builds and passes its tests here, and speed is measured but not promised.

## Python

```sh
pip install kwker
python -m kwker doctor          # prints the engine in use
```

The wheel is `manylinux_2_34_aarch64` (glibc 2.34 or newer). Kwker picks SVE or SVE2 where the CPU has it (Graviton3
and 4, Neoverse V1, V2 and N2, Grace) and NEON elsewhere.

For the PyTorch extensions, the KwkCNN / KwkEncoder / KwkDecoder runners and the ONNX Runtime provider, install the
wheel built for your torch release, for example `kwker==0.1.0+torch2.14`. Their results match PyTorch within each
precision's tolerance; on ARM64 they are not tuned for speed yet.

## C and C++

Use the `kwker-c-0.1.0-linux-aarch64.tar.gz` archive or the arm64 `.deb` / aarch64 `.rpm` packages. They hold the
same files as on x86-64: see [Installation](https://kwker.io/docs/md/install.md#c-and-c).

## Rust

```toml
[dependencies]
kwker = "0.1"
```

The build compiles the NEON and SVE engines with your C++ compiler (GCC or Clang).

## Known issues

- The model runners (KwkCNN, KwkEncoder, KwkDecoder and the ONNX Runtime provider) use the x86 kernels translated to NEON:
  their results match PyTorch and ONNX Runtime, but they are not tuned for speed on ARM64 yet.

## Related

- [Installation](https://kwker.io/docs/md/install.md): every platform and package.
- [Runtime controls](https://kwker.io/docs/md/cpu-runtime.md): see and choose the engine.