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tensorrt-llm

Optimize LLM inference on NVIDIA GPUs with TensorRT and quantization.

Updated Jun 9, 2026
Search Tags:#llm#performance optimization#gpu#inference#nvidia#tensorrt
One-click install
npx skills add https://github.com/zhouboyu-xreal/Hermes-Memory --skill tensorrt-llm-zhouboyu-xreal
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: tensorrt-llm
Source: https://github.com/zhouboyu-xreal/Hermes-Memory/tree/main/optional-skills/mlops/tensorrt-llm
Command: npx skills add https://github.com/zhouboyu-xreal/Hermes-Memory --skill tensorrt-llm-zhouboyu-xreal

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires tensorrt-llm, torch, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill addresses the need for high-performance, low-latency LLM inference, particularly on NVIDIA GPUs, optimizing for production deployment and efficient model serving.

Core Features & Use Cases

  • Optimized Inference: Leverages NVIDIA TensorRT for optimized LLM inference on NVIDIA GPUs.
  • High Throughput: Achieves 10-100x faster inference than PyTorch.
  • Low Latency: Suitable for real-time applications.
  • Quantization: Supports quantization for model compression and acceleration.
  • Batching & Scaling: Offers in-flight batching and multi-GPU scaling for high throughput.
  • Use Case: Ideal for serving models with quantization (FP8/INT4), in-flight batching, and multi-GPU scaling in production environments.

Quick Start

To use TensorRT-LLM, install the package with pip install tensorrt_llm==1.2.0rc3 and run the inference server with trtllm-serve meta-llama/Meta-Llama-3-8B --tp_size 4 --max_batch_size 256 --max_num_tokens 4096.

Frequently Asked Questions about tensorrt-llm

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I optimize LLM inference performance on NVIDIA GPUs for production?▼

Optimize LLM inference on NVIDIA GPUs using TensorRT to achieve maximum throughput and lowest latency. This approach supports production deployment with quantization, in-flight batching, and multi-GPU scaling.

Can I use in-flight batching and multi-GPU scaling for high-throughput model serving?▼

Yes, in-flight batching and multi-GPU scaling are supported for high-throughput model serving. You can configure multi-GPU scaling using the `--tp_size` parameter when launching the inference server.

What's the best way to speed up LLM inference compared to PyTorch?▼

Using TensorRT for LLM inference achieves 10-100x faster inference than PyTorch. It leverages NVIDIA GPU optimization techniques like quantization and in-flight batching to deliver high throughput and low latency.

Does TensorRT LLM inference support quantization for model compression?▼

Yes, TensorRT LLM inference supports quantization for model compression and acceleration. It handles FP8 and INT4 quantization formats, making it ideal for serving compressed models in production environments.

Do I need CUDA and TensorRT to run LLM inference with this optimization?▼

Yes, CUDA and TensorRT are required to run this LLM inference optimization. You also need Python libraries like tensorrt_llm and torch installed in your environment to execute the inference server.

How do I start serving a model like Meta-Llama-3-8B with TensorRT?▼

Install the package with `pip install tensorrt_llm==1.2.0rc3` and run the inference server using `trtllm-serve meta-llama/Meta-Llama-3-8B --tp_size 4 --max_batch_size 256 --max_num_tokens 4096`.

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