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tensorrt-llm

Optimize large language model inference on NVIDIA GPUs with TensorRT and PyTorch.

Updated Apr 30, 2026
Search Tags:#llm#nvidia#inference optimization#high throughput#tensorrt
One-click install
npx skills add https://github.com/Ced3-han/Harness-Settings --skill tensorrt-llm-ced3-han
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: tensorrt-llm
Source: https://github.com/Ced3-han/Harness-Settings/tree/main/skills/tensorrt-llm
Command: npx skills add https://github.com/Ced3-han/Harness-Settings --skill tensorrt-llm-ced3-han

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 challenge of optimizing large language model (LLM) inference for high performance and low latency on NVIDIA GPUs, particularly for production deployment.

Core Features & Use Cases

  • Inference Optimization: Delivers 10-100x faster inference than PyTorch on NVIDIA GPUs.
  • Quantization: Supports FP8 and INT4 quantization for increased speed and reduced memory usage.
  • Batch Inference: Enables efficient in-flight batching for high throughput.
  • Multi-GPU Scaling: Scales inference across multiple GPUs or nodes for large models.

Quick Start

To optimize inference for the Meta-Llama-3-8B model, run:

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 on NVIDIA GPUs?▼

Optimize LLM inference for high throughput on NVIDIA GPUs by using TensorRT-LLM, which supports in-flight batching and quantization to achieve 10-100x faster performance than PyTorch.

What is the best way to reduce LLM latency and memory usage during production deployment?▼

To reduce LLM latency and memory usage during production deployment, apply TensorRT-LLM quantization techniques like FP8 or INT4 to decrease memory consumption and increase inference speed.

Does TensorRT-LLM require PyTorch and TensorRT to run batch inference?▼

TensorRT-LLM requires both the TensorRT-LLM framework and PyTorch dependencies. These tools work together to enable efficient in-flight batching for high throughput LLM inference.

Can I scale large language model inference across multiple GPUs?▼

You can scale large language model inference across multiple GPUs or nodes using TensorRT-LLM, which is designed specifically to handle large models requiring advanced multi-GPU performance.

How does TensorRT-LLm compare to PyTorch for large language model inference?▼

TensorRT-LLM delivers 10-100x faster large language model inference than PyTorch on NVIDIA GPUs, targeting production environments requiring advanced inference performance and low latency.

Why does LLM inference need quantization like FP8 or INT4?▼

LLM inference needs quantization like FP8 or INT4 to significantly increase processing speed and reduce memory usage, making high throughput model deployment on NVIDIA GPUs more efficient.

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