Principle:Triton inference server Server TRT LLM Environment Setup
Metadata
| Field | Value |
|---|---|
| Type | Principle |
| Principle_type | External Tool Doc |
| Workflow | LLM_Deployment_With_TRT_LLM |
| Repo | Triton_inference_server_Server |
| Source | docs/getting_started/llm.md:L52-63 |
| Domains | NLP, LLM_Deployment, Environment_Setup |
| Knowledge_Sources | TRT-LLM Docs|https://nvidia.github.io/TensorRT-LLM/, source::Repo|Triton Server|https://github.com/triton-inference-server/server |
| implemented_by | Implementation:Triton_inference_server_Server_Pip_Install_Tensorrt_LLM |
| 2026-02-13 17:00 GMT |
Overview
Process of installing TensorRT-LLM and its dependencies to enable LLM engine building and inference.
Description
TensorRT-LLM requires a specific Python/CUDA environment with NVIDIA's custom PyPI index. The setup includes installing openmpi, git-lfs, and the tensorrt_llm package with version pinning. This is the foundational step before any model conversion or engine compilation can take place.
The installation process involves:
- Installing system-level dependencies such as openmpi-bin, libopenmpi-dev, git, and git-lfs
- Configuring Python 3.10 with pip
- Installing the tensorrt_llm Python package from NVIDIA's custom PyPI index with explicit version pinning
- Verifying the installation by importing the module in Python
The NVIDIA custom PyPI index (https://pypi.nvidia.com) hosts pre-built wheels for TensorRT-LLM that include CUDA-specific binaries, ensuring compatibility with the target GPU hardware.
Usage
This principle is applied at the very beginning of any TRT-LLM deployment workflow. It must be completed before model weight download, checkpoint conversion, or engine building can proceed. The environment setup is typically performed once per deployment host or container image build.
Workflow context:
- Precedes: Principle:Triton_inference_server_Server_Model_Weight_Download, Principle:Triton_inference_server_Server_Weight_Conversion
- Depends on: NVIDIA GPU drivers, CUDA toolkit 12.4+
Theoretical Basis
The dependency chain for TRT-LLM follows a strict ordering:
CUDA drivers → CUDA toolkit → Python 3.10 → TensorRT-LLM → model conversion tools
Each layer depends on the one below it:
- CUDA drivers provide low-level GPU access
- CUDA toolkit provides compilation and runtime libraries
- Python 3.10 is the required interpreter version for TRT-LLM wheel compatibility
- TensorRT-LLM provides the model conversion, engine building, and inference APIs
- Model conversion tools (included in TRT-LLM) transform framework-specific weights into TRT-LLM checkpoint format
The use of openmpi is required for multi-GPU inference scenarios where tensor parallelism distributes model layers across multiple GPUs via MPI-based communication.
Related Pages
- Implementation:Triton_inference_server_Server_Pip_Install_Tensorrt_LLM
- Principle:Triton_inference_server_Server_Model_Weight_Download — Next step after environment setup
- Principle:Triton_inference_server_Server_Weight_Conversion — Requires TRT-LLM installation
- Principle:Triton_inference_server_Server_TensorRT_Engine_Build — Requires TRT-LLM installation