Building llama.cpp with CUDA in an NVIDIA HPC SDK Environment

If you are working in an NVIDIA HPC SDK environment and want to build llama.cpp with CUDA support, one reliable approach is to use GCC/G++ for the C/C++ parts and NVCC for the CUDA parts.

This setup is practical because some compiler warning flags used by projects like ggml/llama.cpp are commonly supported by GCC/Clang, but may not be accepted by other C++ compilers. By explicitly selecting gcc and g++, you reduce the risk of compiler-flag incompatibilities, while still enabling CUDA with nvcc.

Recommended CMake Commands

Run the following commands from the project root directory:

cmake -S . -B build \
  -DGGML_CUDA=ON \
  -DCMAKE_C_COMPILER=gcc \
  -DCMAKE_CXX_COMPILER=g++ \
  -DCMAKE_CUDA_COMPILER=nvcc

cmake --build build -j

What These Options Do

• -DGGML_CUDA=ON
  Enables CUDA support in the ggml backend used by llama.cpp.

• -DCMAKE_C_COMPILER=gcc and -DCMAKE_CXX_COMPILER=g++
  Ensures the C and C++ source files are compiled with GCC/G++, which typically supports the warning and build flags used by many open-source projects.

• -DCMAKE_CUDA_COMPILER=nvcc
  Uses NVIDIA’s CUDA compiler for CUDA code, ensuring proper GPU compilation.

• cmake --build build -j
  Builds the project using parallel jobs (the build system decides how many), which usually speeds up compilation.

Summary

In an NVIDIA HPC SDK environment, explicitly selecting gcc/g++ for host compilation and nvcc for CUDA compilation is a simple and effective way to build llama.cpp with CUDA enabled. This approach is also easy to reproduce across systems and tends to avoid compiler option conflicts.