Exec Local Compile Agent Skill
Compile TensorRT-LLM on a compute node inside a Docker container. Use this when already on a compute node with GPUs visible.
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/NVIDIA/TensorRT-LLM --skill exec-local-compileThe instruction itself
13 sections, as written by the authorCompile TensorRT-LLM (Local / Compute Node)
Compile TensorRT-LLM from source on a compute node inside a Docker container.
When to Use
| Scenario | Use This Skill? |
|----------|----------------|
| On a compute node with GPUs visible (nvidia-smi works) | Yes |
| On a SLURM login node (no GPUs) | No — use exec-slurm-compile instead |
Prerequisites
- You are inside a Docker/enroot container on a compute node
nvidia-smisucceeds (GPUs visible)/usr/local/tensorrtexists (TensorRT installation in the container)
Instructions
Step 1: Verify Environment
Run nvidia-smi to confirm you are on a compute node with GPU access.
Step 2: Locate the Codebase
cd to the TensorRT-LLM repository. If the path is not provided by the user, ask for it.
Step 3: (Optional) Checkout Branch
If the user specifies a branch (e.g., "compile ToT"), checkout and pull:
git checkout main && git pull
Step 4: Build
Run the build command (incremental by default — omit -c/--clean unless explicitly requested or the incremental build fails):
./scripts/build_wheel.py --use_ccache -a "<arch>" -f --nvtx
Replace <arch> with the target GPU architecture (see Architecture Reference below). If not specified by the user, auto-detect from nvidia-smi.
Step 5: Install
pip install -e .[devel]
Step 6: Verify
python3 -c "import tensorrt_llm; print(tensorrt_llm.__version__)"
Build Flags
| Flag | Description |
|------|-------------|
| -a "<arch>" | Target GPU architecture(s) |
| --nvtx | Enable NVTX markers for profiling |
| --use_ccache | Use ccache for faster recompilation |
| -f / --fast_build | Skip some kernels for faster dev compilation. Always use for dev builds. |
| -c / --clean | Clean build directory before building. Only when needed (see below). |
| --skip_building_wheel | Build in-place without creating a wheel file |
| --no-venv | Skip virtual environment creation |
Architecture Reference
| Value | GPU Family |
|-------|-----------|
| "100-real" | Blackwell (B200, GB200) |
| "90-real" | Hopper (H100, H200) |
| "89-real" | Ada Lovelace (L40S) |
| "80-real" | Ampere (A100) |
| "90;100-real" | Multiple architectures |
Incremental vs. Clean Builds
Default to incremental builds — CMake only recompiles changed files, saving significant time.
Use a clean build (-c) only when:
- The user explicitly requests a clean/fresh build
- An incremental build fails with linker errors, stale object files, or CMake cache issues
- Major branch changes (e.g., rebasing across many commits) that may invalidate the build cache
- Build system files changed (
CMakeLists.txt,*.cmake)
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How to use it
Take nvidia/exec-local-compile from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.
The instructions reference pip.
Without those the skill loads but fails at the first command.