nvidia-tensorrt-llm-exec-local-compile

name	nvidia-tensorrt-llm-exec-local-compile
description	Compile TensorRT-LLM on a compute node inside a Docker container. Use this when already on a compute node with GPUs visible.
license	Apache-2.0
metadata	{"author":"NVIDIA Corporation"}

Compile 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-smi succeeds (GPUs visible)
• /usr/local/tensorrt exists (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 --trt_root /usr/local/tensorrt --benchmarks --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
--trt_root /usr/local/tensorrt	TensorRT installation path (standard in NVIDIA containers)
--benchmarks	Build the C++ benchmarks
-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)