Developers using NVIDIA's HPC SDK can build llama.cpp with CUDA support by combining GCC/G++ for C/C++ code and NVCC for CUDA kernels. This hybrid compiler approach is recommended because projects like ggml/llama.cpp use compiler flags that GCC accepts but other C++ compilers might reject, reducing build conflicts.

The practical CMake commands involve setting `-DGGML_CUDA=ON` to enable the GPU backend, specifying `gcc` and `g++` for host code, and `nvcc` for CUDA. This explicit configuration avoids common toolchain incompatibilities, making builds more reproducible across different HPC environments where compiler defaults can vary.

For teams deploying large language models, this method streamlines getting llama.cpp running on NVIDIA GPUs. It sidesteps the hassle of modifying project build scripts to accommodate less common compilers. Future updates to llama.cpp's build system may simplify this further, but for now, this explicit compiler selection is a reliable workaround.