Tensorrt Llm — High-throughput LLM inference on NVIDIA GPUs
Tensorrt Llm — High-throughput LLM inference on NVIDIA GPUs
Start with meaning, then move to detail.
This lesson explains Tensorrt Llm — High-throughput LLM inference on NVIDIA GPUs as part of getting started with Hermes correctly. You will learn what it does, when it matters, and the smallest safe test that proves it works.
If you are new, do not memorize names. Focus on three questions: what problem does this solve, what access does it need, and how can you verify the result?
For practice, inspect the first example, identify its effects, run it on test data, and compare the result with the source claim.
For advanced readers, inspect Skill metadata, Reference: full SKILL.md, When to use TensorRT-LLM, then verify failure modes and version compatibility.
No prior experience is required; follow the steps on a safe test setup first.
A clear outcome before you read.
- Understand Tensorrt Llm — High-throughput LLM inference on NVIDIA GPUs without assumed prior knowledge.
- Separate the source description from what still needs testing in your environment.
- Read the first command and identify its inputs and outputs before copying it.
Short definitions before the details.
- Skill
- An instruction bundle that teaches Hermes a repeatable workflow without necessarily adding an external service.
High-throughput LLM inference on NVIDIA GPUs
What does the source say, and in what order?
- 01Skill metadata
Start here to understand the core idea or structure.
- 02Reference: full SKILL.md
Read this after the foundation, then connect it to the previous step.
- 03When to use TensorRT-LLM
Read this after the foundation, then connect it to the previous step.
- 04Quick start
Read this after the foundation, then connect it to the previous step.
- 05Installation
Read this after the foundation, then connect it to the previous step.
- 06Basic inference
Read this after the foundation, then connect it to the previous step.
- 07Serving with trtllm-serve
Read this after the foundation, then connect it to the previous step.
- 08Key features
Read this after the foundation, then connect it to the previous step.
- 09Performance optimizations
Read this after the foundation, then connect it to the previous step.
- 10Parallelism
Finish here to verify the result and special cases.
Copy only after you understand the effect.
# Docker (recommended) — images are on NGC (nvcr.io), not Docker Hub.
# Replace x.y.z with the desired version (e.g. 1.2.1). Browse tags on NGC:
# https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tensorrt-llm/containers/release/tags
docker pull nvcr.io/nvidia/tensorrt-llm/release:x.y.z
# pip install (current stable GA)
pip install tensorrt_llm
# Requires CUDA 13.2.1, TensorRT 10.x, Python 3.10-3.12### Serving with trtllm-serve## Key features
### Performance optimizations
- **In-flight batching**: Dynamic batching during generation
- **Paged KV cache**: Efficient memory management
- **Flash Attention**: Optimized attention kernels
- **Quantization**: FP8, INT4, FP4 for 2-4× faster inference
- **CUDA graphs**: Reduced kernel launch overhead
### Parallelism
- **Tensor parallelism (TP)**: Split model across GPUs
- **Pipeline parallelism (PP)**: Layer-wise distribution
- **Expert parallelism**: For Mixture-of-Experts models
- **Multi-node**: Scale beyond single machine
### Advanced features
- **Speculative decoding**Read the first command and identify its inputs and outputs before copying it.
Match every command to your installed Hermes version, review the files and accounts it can reach, and use non-sensitive data for the first test. If this explanation differs from the source, the official source wins.