---
title: "TensorRT-LLM & Hardware-Specific Optimization"
description: "How TensorRT-LLM builds an engine ahead of time for one GPU and workload: the artifact, fused kernels in a decode step, its cost, and precision by GPU."
canonical_url: "https://fanout.sh/inference-eng/curriculum/17-03"
md_url: "https://fanout.sh/inference-eng/curriculum/17-03.md"
access: "pro-preview"
---

# TensorRT-LLM & Hardware-Specific Optimization

How TensorRT-LLM builds an engine ahead of time for one GPU and workload: the artifact, fused kernels in a decode step, its cost, and precision by GPU.

- Access: Fanout Pro (public preview)

## Public preview

The two engines of the previous lesson settle what suits the card while they start. A third design settles earlier. It takes the weights, the machine they will run on, and the shape of the traffic expected, and turns all three into an artifact before the first request arrives.

## In this lesson

- The build, and what it produces
- Kernels, fusion, and one decode step
- The price of the artifact
- Where the default moved
- Precision, by architecture

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