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tensorrt-llm

Optimize LLM inference with NVIDIA TensorRT for high throughput and low latency.

Updated May 11, 2026
Search Tags:#production#low latency#fp8#nvidia#inference optimization#high throughput#tensorrt-llm#int4#inference serving#multi-gpu scaling#in-flight batching
One-click install
npx skills add https://github.com/richardnguyen0715/keep-it-real --skill tensorrt-llm-richardnguyen0715
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: tensorrt-llm
Source: https://github.com/richardnguyen0715/keep-it-real/tree/main/refer-projects/hermes-agent/optional-skills/mlops/tensorrt-llm
Command: npx skills add https://github.com/richardnguyen0715/keep-it-real --skill tensorrt-llm-richardnguyen0715

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the challenge of achieving high-throughput and low-latency inference for large language models, especially on NVIDIA GPUs, using NVIDIA TensorRT.

Core Features & Use Cases

  • Optimized Inference: Achieve up to 10-100x faster inference than PyTorch.
  • Quantization: Support for quantization (FP8/INT4) for increased performance and reduced memory usage.
  • Multi-GPU Scaling: Leverage multi-GPU environments for scaling up inference capabilities.
  • Use Case: Ideal for deploying production-grade language models that require high throughput and low latency, such as in chatbots or API endpoints.

Quick Start

Use the tensorrt-llm skill to optimize inference for a pre-trained language model.

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 lower latency and higher throughput on NVIDIA GPUs?▼

You can optimize LLM inference using NVIDIA TensorRT to achieve high throughput and low latency, targeting up to 10-100x faster inference than standard PyTorch in production environments.

What's the best way to reduce memory usage during LLM inference serving?▼

Reducing memory usage during LLM inference serving is achievable through TensorRT quantization techniques like FP8 and INT4, which simultaneously increase performance and lower memory requirements.

Does TensorRT support multi-GPU scaling for production inference?▼

Yes, TensorRT supports multi-GPU scaling for production inference, allowing you to leverage multi-GPU environments to scale up inference capabilities for large language models.

Can I use FP8 or INT4 quantization to speed up inference on CUDA?▼

You can use FP8 or INT4 quantization with TensorRT on CUDA to speed up inference, reducing memory usage while maintaining model performance for production-grade applications.

Do I need specific hardware to run TensorRT inference optimization?▼

You need CUDA and TensorRT support with NVIDIA GPUs to run this inference optimization, as the Skill specifically targets production environments equipped with NVIDIA hardware.

What are the limitations of using PyTorch compared to TensorRT for inference?▼

Standard PyTorch lacks native in-flight batching and deep quantization optimizations, whereas TensorRT provides 10-100x faster inference but requires specific CUDA and NVIDIA GPU hardware.

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