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tensorrt-llm

Optimize large language model inference with NVIDIA TensorRT on GPU hardware.

2|Updated Jun 8, 2026
Search Tags:#LLM inference#low latency#TensorRT#inference optimization#high throughput#GPU scaling
One-click install
npx skills add https://github.com/vikrant-project/devil-agent-ai-platform --skill tensorrt-llm-vikrant-project
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: tensorrt-llm
Source: https://github.com/vikrant-project/devil-agent-ai-platform/tree/main/agent_core/optional-skills/mlops/tensorrt-llm
Command: npx skills add https://github.com/vikrant-project/devil-agent-ai-platform --skill tensorrt-llm-vikrant-project

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 provides maximum inference throughput and low latency for Large Language Models using NVIDIA TensorRT, ideal for high-performance production deployment.

Core Features & Use Cases

  • High Throughput: Accelerate LLM inference up to 10-100x compared to PyTorch.
  • Low Latency: Optimize for real-time applications.
  • Quantization: Supports quantization for models (FP8/INT4) for efficient inference.
  • Scalability: Scale across multiple GPUs or nodes for increased performance.
  • Use Case: Enhance the performance of LLM-based services like chatbots or content generation for AI-assisted applications.

Quick Start

Use the tensorrt-llm skill to serve an LLM model on a NVIDIA GPU using the latest TensorRT library.

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?▼

You can optimize LLM inference for high throughput and low latency by using NVIDIA TensorRT to accelerate models, achieving up to 10-100x performance improvements over standard PyTorch execution.

Can I use TensorRT to scale LLM inference across multiple GPUs?▼

Yes, you can use TensorRT to scale LLM inference efficiently across multiple GPUs or nodes, enabling increased performance for high-throughput production environments.

What's the best way to reduce LLM inference latency for real-time applications?▼

The best way to reduce LLM inference latency is to optimize models with NVIDIA TensorRT, which supports efficient quantization like FP8 and INT4 to accelerate real-time application performance.

Do I need NVIDIA GPU hardware to run TensorRT inference optimization?▼

Yes, you need NVIDIA GPU hardware because TensorRT inference optimization is specifically designed to maximize LLM performance and enable efficient quantization on NVIDIA GPU architectures.

How does quantization improve LLM inference performance?▼

Quantization improves LLM inference performance by reducing model precision to formats like FP8 or INT4, which significantly decreases memory usage and accelerates computation on GPU hardware.

Why is my PyTorch LLM inference too slow for production chatbots?▼

PyTorch LLM inference is slow for production chatbots because it lacks hardware-level optimizations; transitioning to TensorRT maximizes throughput and reduces latency for real-time services.

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