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tensorrt-llm

Compile and serve LLMs with NVIDIA TensorRT-LLM for reduced latency.

1|Updated May 16, 2026
Search Tags:#model serving#llm inference#tensorrt-llm#nvidia gpu#fp8 quantization#int4 deployment#multi-gpu scaling
One-click install
npx skills add https://github.com/devMoez/titan --skill tensorrt-llm-devmoez
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: tensorrt-llm
Source: https://github.com/devMoez/titan/tree/main/optional-skills/mlops/tensorrt-llm
Command: npx skills add https://github.com/devMoez/titan --skill tensorrt-llm-devmoez

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

TensorRT-LLM solves slow, high-latency LLM inference by optimizing the model execution for NVIDIA GPUs to maximize throughput and minimize time-to-first-token.

Core Features & Use Cases

  • High-throughput inference serving: Use in-flight batching, paged KV cache, and optimized attention kernels to keep GPUs saturated under real traffic.
  • Low-latency production endpoints: Run an OpenAI-compatible server (trtllm-serve) for chat/completions with streaming and health/metrics.
  • Efficient deployment at scale: Use FP8/INT4 quantization plus tensor/pipeline/expert parallelism for multi-GPU and multi-node deployments (A100/H100-class).

Quick Start

Start an OpenAI-compatible chat server for an NVIDIA-optimized deployment by running one trtllm-serve command for your model with tensor parallelism and an appropriate dtype like fp8.

Frequently Asked Questions about tensorrt-llm

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

FAQPage Schema
How do I speed up LLM inference on NVIDIA GPUs?▼

Speed up LLM inference on NVIDIA GPUs by compiling models with TensorRT-LLM, which uses optimized attention kernels, paged KV cache, and in-flight batching to maximize throughput and minimize latency. This approach significantly increases tokens-per-second and reduces time-to-first-token.

What is the best way to deploy an OpenAI-compatible LLM server with FP8 quantization?▼

Deploy an OpenAI-compatible LLM server with FP8 quantization by running the trtllm-serve command for your model. This enables low-latency production endpoints for chat and completions with streaming, utilizing NVIDIA hardware optimizations for efficient execution.

Can I use TensorRT-LLM for multi-GPU and multi-node scaling?▼

Yes, you can use TensorRT-LLM for multi-GPU and multi-node scaling on A100 or H100-class GPUs. It supports tensor, pipeline, and expert parallelism alongside FP8 or INT4 quantization to ensure efficient deployment and saturated GPU usage under heavy traffic.

Do I need a specific environment to run TensorRT-LLM for production serving?▼

Yes, running TensorRT-LLM for production serving requires a valid CUDA and TensorRT environment. You must configure correct dtype and quantization settings, such as FP8 or INT4, and use either the trtllm-serve command or the TensorRT-LLM Python API.

How does in-flight batching improve LLM serving throughput?▼

In-flight batching improves LLM serving throughput by dynamically managing incoming requests alongside paged KV cache and optimized attention kernels. This keeps NVIDIA GPUs fully saturated under real traffic, drastically reducing latency and increasing overall tokens-per-second.

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