The engine is a pre-built binary, bundled in the image and built on a host with the
CUDA toolkit — never compiled per-request in the CUDA-*runtime* container (which has
no nvcc). Build it with the host CUDA 13.x toolkit like ds4: the binary links
libcudart.so.13, resolved in-container from /opt/coderai/local-libs (the CUDA-13
runtime coderai already ships for PyTorch), so it adds no portability constraint.

- worker: _detect_build_target now needs a REAL nvcc (a runtime container has
  /usr/local/cuda but no compiler); ensure_built fails early with a clear
  "pre-compile the binary" message instead of cloning and dying deep in `make` with
  the confusing "nvcc not found … backend_cuda.o Error". _make_args defaults CUDA to
  a PORTABLE arch (sm_80..120 + PTX) and points at the resolved nvcc/CUDA_HOME.
- build.sh --colibri: require a real nvcc and default CUDA_ARCH=portable (like ds4's
  cuda-generic), so the bundled binary isn't locked to the build host's GPU.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01LoSpEthysqmseCc6Geizty