# Linux Build

This document covers direct CMake builds on Linux. For the quick script-based path, see the build section in the main README.

## Requirements

- GCC 13 or newer
- CMake
- A supported backend toolchain when enabling CUDA or Vulkan
- For CUDA: CUDA Toolkit 12.0 or newer — any 12.x or 13.x is fine (the Docker image
  pins 12.9, and a CUDA 13 package is documented for Windows). The real upper bound
  is the host GCC the chosen toolkit accepts, not the CUDA version: e.g. CUDA 12.9
  supports up to GCC 14

Native ggml CPU optimization is enabled by default for local performance. If your compiler or assembler rejects a generated CPU instruction such as `vpdpbusd`, reconfigure with `-DENGINE_ENABLE_NATIVE_CPU=OFF` to build portable CPU kernels.

If you use an environment manager or custom toolchain, activate it before running the commands below.

## Configure

CPU-only:

```bash
cmake -S . -B build
```

CUDA:

```bash
cmake -S . -B build -DENGINE_ENABLE_CUDA=ON
```

Without `CMAKE_CUDA_ARCHITECTURES`, the portable default arch list is built:
works on many GPUs, but builds slower. To build only for the local GPUs,
`native` is recommended (CMake >= 3.24; on older CMake it falls back to the
portable list) or set an arch manually.

```bash
cmake -S . -B build -DENGINE_ENABLE_CUDA=ON -DCMAKE_CUDA_ARCHITECTURES=native
```

CMake picks the first `nvcc` on `PATH`, which is often **not** the toolkit you want:
distro packages install an old one to `/usr/bin/nvcc` (Ubuntu 22.04's
`nvidia-cuda-toolkit` is CUDA 11.5) while the toolkit from NVIDIA lands in
`/usr/local/cuda-<version>`. Point at it explicitly, and set the architecture of the
GPU you are building for, rather than relying on `PATH`:

```bash
cmake -S . -B build -DENGINE_ENABLE_CUDA=ON \
  -DCUDAToolkit_ROOT=/usr/local/cuda-12.9 \
  -DCMAKE_CUDA_COMPILER=/usr/local/cuda-12.9/bin/nvcc \
  -DCMAKE_CUDA_ARCHITECTURES=86      # 86 = RTX 3000, 89 = RTX 4000, 120 = Blackwell
```

Set **both**. `CMAKE_CUDA_COMPILER` only chooses the compiler, and `CUDAToolkit_ROOT`
only chooses the libraries — with a distro CUDA also installed, setting just the
compiler produces a build that compiles with the new `nvcc` but silently links the
old `libcudart`/`libcublas` from `/usr/lib/x86_64-linux-gnu` (owned by
`nvidia-cuda-dev`). That mismatch links without warning. Check the result:

```bash
readelf -d build/bin/audiocpp_server | grep NEEDED | grep cuda
# want libcudart.so.12 / libcublas.so.12 — libcudart.so.11.0 means a mixed build
```

Leave `CMAKE_CUDA_ARCHITECTURES` unset to build the portable default arch list
(works on many GPUs, slower to build); set it to `native` (CMake >= 3.24) to
build only for the GPUs present at build time. Note that CMake caches the CUDA
compiler: switching toolkits in an existing build directory requires deleting
`CMakeCache.txt` and `CMakeFiles/`.


Old CUDA GPUs (cm<89):

If you build for old CUDA GPUs with CUDA architecture < 8.9, e.g. Pascal compute-capability
6.1, use build_linux.sh with  `--cuda-arch`. Or cmake with `-DCMAKE_CUDA_ARCHITECTURES=<archi-id>` 
(and un-defines the sticky cache value so a previous configure does not win):

```bash
./scripts/build_linux.sh --backend cuda --cuda-arch 61 --target audiocpp_cli --target audiocpp_server
```
or
```bash
cmake -S . -B build -DENGINE_ENABLE_CUDA=ON \
  -DCUDAToolkit_ROOT=/usr/local/cuda-12.9 \
  -DCMAKE_CUDA_COMPILER=/usr/local/cuda-12.9/bin/nvcc \
  -DCMAKE_CUDA_ARCHITECTURES=61      # 61 = GTX 1080Ti
```

On WSL2, install the toolkit only — `cuda-toolkit-<version>` from the `wsl-ubuntu`
repo. The `cuda` and `cuda-drivers` metapackages pull a Linux display driver that
breaks the GPU passthrough provided by the Windows host driver.

Jetson Orin (aarch64, compute capability 8.7 — Orin Nano, Orin NX, AGX Orin):

Tested on JetPack 7.2 / CUDA 13.2, both an Orin Nano 8GB and an Orin NX 16GB, full
model catalog. `87` is intentionally not in this doc's example list above — always
pass it explicitly:

```bash
cmake -S . -B build -DENGINE_ENABLE_CUDA=ON \
  -DCUDAToolkit_ROOT=/usr/local/cuda \
  -DCMAKE_CUDA_COMPILER=/usr/local/cuda/bin/nvcc \
  -DCMAKE_CUDA_ARCHITECTURES=87-real
```

On an 8GB board (Orin Nano), also add `-DGGML_CUDA_NO_VMM=ON`. Several model
families otherwise crash at `cuMemAddressReserve` in ggml's CUDA pool allocator,
which reserves a 32GB virtual address range unconditionally — this fails on the
Nano's smaller unified-memory address space even though it's never meant to be
physically backed. **Only add this on 8GB-class boards** — A/B tested on a 16GB
Orin NX, where it isn't needed, it costs a real (if small) ~2% throughput
regression rather than being a free no-op.

If a specific model still fails with a Tegra `NvMapMemAllocInternalTagged`
allocation error on an 8GB board despite the flag above, even for a small
allocation with apparent memory headroom to spare, try a reboot before
concluding the model doesn't fit — this can be boot-persistent NVMAP allocator
fragmentation from a prior long-running session, not a hard capacity limit.

Two environment gaps seen on a bare Nano image, neither audio.cpp's fault:
`cmake` may not be installed at all and there's no root to `apt install` one — the
official Kitware prebuilt aarch64 tarball (`cmake.org/download`) works fine
extracted to `~/.local/` with no elevation needed. `nvidia-smi` reports memory as
`N/A` on Jetson's unified-memory architecture — use `/proc/meminfo` or
`tegrastats` for RAM tracking instead, not GPU-memory tooling built for discrete
cards.

Vulkan:

```bash
cmake -S . -B build -DENGINE_ENABLE_VULKAN=ON
```

Buffer bindings larger than the device's `maxStorageBufferRange` require both
Vulkan headers and a driver supporting `VK_EXT_shader_64bit_indexing`. Use a recent
Vulkan SDK to enable this support at build time. Oversized bindings select the
64-bit indexing pipeline; bindings within the limit keep the existing pipeline.
Older headers and devices retain their existing size restrictions.

Portable CPU-kernel fallback:

```bash
cmake -S . -B build -DENGINE_ENABLE_NATIVE_CPU=OFF
```

## Build

Build the CLI and server from the configured tree:

```bash
cmake --build build -j$(nproc) --target audiocpp_cli --target audiocpp_server
```

If your machine is memory-constrained, use a smaller `-j` value, for example `-j4`.

## Build Type Notes

- For single-config generators, the recommended config is `RelWithDebInfo`
- For multi-config generators, choose the configuration at build time
- Backend and feature options are independent from build type