skills.rest
aayushsoam
aayushsoamCOMMUNITY
Explore all skills by this author →
GitHub

tensorrt-llm

Optimize LLM inference on NVIDIA GPUs with TensorRT.

1|1|Updated May 25, 2026
Search Tags:#llm#gpu#inference#nvidia#tensorrt
One-click install
npx skills add https://github.com/aayushsoam/clawbot-agent --skill tensorrt-llm-aayushsoam
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: tensorrt-llm
Source: https://github.com/aayushsoam/clawbot-agent/tree/main/optional-skills/mlops/tensorrt-llm
Command: npx skills add https://github.com/aayushsoam/clawbot-agent --skill tensorrt-llm-aayushsoam

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 optimizes Large Language Model (LLM) inference with NVIDIA TensorRT, providing maximum throughput and lowest latency for production deployment on NVIDIA GPUs.

Core Features & Use Cases

  • High Throughput: Achieve 10-100x faster inference than PyTorch on NVIDIA GPUs.
  • Low Latency: Ideal for real-time applications with low latency requirements.
  • Quantization: Supports FP8 and INT4 quantization for faster inference and reduced memory usage.
  • Batching: Enables in-flight batching for maximum throughput.
  • Multi-GPU Scaling: Scale across multiple GPUs or nodes for increased performance.
  • Use Case: Deploy TensorRT-LLM on NVIDIA GPUs (A100/H100) for production environments where fast inference is critical.

Quick Start

Use the tensorrt-llm skill to deploy a Meta-Llama-3-8B model with TensorRT optimization on your NVIDIA GPU for fast inference.

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 on NVIDIA GPUs for lower latency?▼

To optimize LLM inference on NVIDIA GPUs for lower latency, you can use TensorRT to apply high-throughput engine compilation, in-flight batching, and FP8 or INT4 quantization, achieving significantly faster performance than standard PyTorch execution.

What is the best way to speed up PyTorch LLM models for production environments?▼

The best way to speed up PyTorch LLM models for production is compiling them with TensorRT. This applies in-flight batching and hardware-level optimizations to achieve 10-100x faster inference on NVIDIA GPUs.

Does TensorRT LLM optimization support INT4 and FP8 quantization formats?▼

Yes, TensorRT LLM optimization supports both FP8 and INT4 quantization formats. These formats reduce memory usage and accelerate inference speed on supported NVIDIA GPUs.

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

Yes, you can use TensorRT to scale LLM inference across multiple GPUs or nodes. This multi-GPU scaling capability increases overall throughput for demanding production workloads.

Do I need CUDA and Python to run TensorRT optimized LLM models?▼

Yes, you need CUDA, Python, and TensorRT installed to run optimized LLM models. These dependencies are required to execute the inference engines on NVIDIA hardware.

Related Skills

Explore More Software Engineering Skills→
ovachieverovachiever
52

tensorrt-llm

Optimize LLM inference on NVIDIA GPUs with TensorRT-LLM.

Community
Advanced
jayll1303jayll1303
17

tensorrt-llm

Convert HuggingFace checkpoints into optimized TensorRT-LLM engines with quantization.

Community
Advanced
NVIDIANVIDIA
13.2k

trt-torch-quickstart

Convert PyTorch models to TensorRT engines using torch_tensorrt.

Official
Intermediate
shaharKeisarAppsshaharKeisarApps

gradle-expert

Configure Gradle Kotlin DSL for Kotlin Multiplatform projects with version catalogs.

Community
Advanced