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tensorrt-llm

Optimize LLM inference on NVIDIA GPUs with TensorRT-LLM.

1|Updated Apr 4, 2026
Search Tags:#latency#gpu#throughput#quantization#inference#serving#tensorrt-llm
One-click install
npx skills add https://github.com/m0at/claudemd --skill tensorrt-llm-m0at
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: tensorrt-llm
Source: https://github.com/m0at/claudemd/tree/main/skills/tensorrt-llm
Command: npx skills add https://github.com/m0at/claudemd --skill tensorrt-llm-m0at

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

NVIDIA GPU inference with large language models often suffers from suboptimal throughput and high latency; TensorRT-LLM addresses this by providing optimized inference with FP8/INT4 quantization, in-flight batching, and multi-GPU scaling.

Core Features & Use Cases

  • In-flight batching
  • Paged KV cache
  • CUDA graphs
  • FP8/INT4 quantization
  • Tensor, pipeline, and expert parallelism
  • Multi-node deployment
  • Production-grade serving

Quick Start

Install tensorrt_llm and initialize an LLM with a model to begin generation.

Frequently Asked Questions about tensorrt-llm

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I reduce LLM inference latency on NVIDIA GPUs?▼

To reduce LLM inference latency on NVIDIA GPUs, apply FP8 or INT4 quantization alongside in-flight batching. These optimizations streamline model execution to significantly decrease request processing time.

What is the best way to maximize LLM throughput on A100 and H100 deployments?▼

Maximizing LLM throughput on A100 and H100 deployments involves utilizing paged KV cache and CUDA graphs. These features optimize memory usage and kernel launches to handle higher concurrent request volumes efficiently.

Can I scale LLM serving across multiple GPUs and nodes?▼

You can scale LLM serving across multiple GPUs and nodes using tensor, pipeline, and expert parallelism. This distributes model weights and computation across multi-node deployments for production-grade serving.

Does TensorRT-LLM support INT4 and FP8 quantization for production inference?▼

Yes, TensorRT-LLM supports INT4 and FP8 quantization for production inference. Applying these quantization methods reduces memory footprint and accelerates computation while maintaining model accuracy.

Why does LLM inference throughput suffer on NVIDIA GPUs without TensorRT?▼

LLM inference throughput suffers without TensorRT due to suboptimal memory management and lack of in-flight batching. Standard runtimes often fail to efficiently schedule concurrent requests or optimize kernel execution.

Do I need CUDA-compatible hardware to use in-flight batching for LLMs?▼

Yes, you need CUDA-compatible hardware to use in-flight batching for LLMs. This feature requires TensorRT-enabled runtimes and NVIDIA GPUs to dynamically manage request scheduling and paged KV cache.

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