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tensorrt-llm

Optimize LLM inference on NVIDIA GPUs using TensorRT-LLM.

3|1|Updated May 19, 2026
Search Tags:#low latency#fp8#nvidia#inference optimization#high throughput#tensorrt#multi-gpu scaling
One-click install
npx skills add https://github.com/Quill-Agent/Quill-Agent --skill tensorrt-llm-quill-agent
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: tensorrt-llm
Source: https://github.com/Quill-Agent/Quill-Agent/tree/main/optional-skills/mlops/tensorrt-llm
Command: npx skills add https://github.com/Quill-Agent/Quill-Agent --skill tensorrt-llm-quill-agent

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the need for optimized LLM inference with NVIDIA TensorRT, providing significant improvements in throughput and latency for NVIDIA GPUs.

Core Features & Use Cases

  • High Throughput and Low Latency: Achieve up to 10-100x faster inference on NVIDIA GPUs compared to PyTorch.
  • Quantization Support: Serve models with quantization (FP8/INT4), enabling more efficient processing.
  • In-Flight Batching: Dynamically batch requests during generation for improved throughput.
  • Multi-GPU Scaling: Scale across multiple GPUs or nodes for increased capacity.
  • Use Case: Ideal for production deployment on NVIDIA GPUs (A100/H100) where high-speed, low-latency inference is crucial.

Quick Start

Run the following command to optimize your LLM inference with TensorRT-LLM on an NVIDIA GPU:

pip install tensorrt_llm==1.2.0rc3

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 for high throughput and low latency on NVIDIA GPUs?▼

Optimize LLM inference using NVIDIA TensorRT to achieve up to 10-100x faster generation on NVIDIA GPUs compared to PyTorch, significantly improving both throughput and latency.

Can I use quantization to speed up large language model inference?▼

Yes, serve large language models using quantization support like FP8 or INT4, enabling more efficient processing and drastically reducing memory footprint during inference.

Does TensorRT support multi-GPU scaling for increased inference capacity?▼

Yes, multi-GPU scaling allows you to scale inference workloads across multiple GPUs or nodes, increasing capacity for large language models on NVIDIA hardware.

What is in-flight batching and how does it improve LLM inference throughput?▼

In-flight batching dynamically groups requests during generation, improving throughput by continuously feeding the GPU with active tokens rather than waiting for batch completion.

Do I need PyTorch and TensorRT installed to optimize my large language model?▼

Yes, dependencies include TensorRT and PyTorch. You must install these frameworks to build optimized engines for large language model inference on NVIDIA GPUs.

What is the best way to deploy LLMs in production on A100 or H100 GPUs?▼

Deploy LLMs in production on A100 or H100 GPUs using TensorRT for optimized inference, leveraging in-flight batching and FP8 quantization for high-speed, low-latency serving.

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