---
title: "TensorRT | Skills We Assign For | Azendo"
description: "TensorRT for inference optimisation — precision trade-offs, engine portability, plus the Azendo roles assigned for it."
url: "https://azendo.co/skills/tensorrt/"
---

[Skills](https://azendo.co/skills/) MLOps and model delivery 

# TensorRT.

TensorRT is NVIDIA's inference optimisation library. It compiles a trained model into an engine tuned for a specific GPU, applying layer fusion, precision reduction and kernel selection to reduce latency and increase throughput.

## Where TensorRT fits on a long engagement.

The speedups are substantial and come from real compilation work: fusing layers, selecting kernels for the specific GPU, and reducing precision where the model tolerates it. For high-volume inference the throughput improvement translates directly into fewer GPUs.

Reduced precision needs validation rather than assumption. FP16 is usually safe; INT8 quantisation requires calibration data and can degrade accuracy in ways that only appear on particular inputs. Measuring accuracy after optimisation on a representative set is not optional.

## What an assigned team does with TensorRT.

Engines are built for a specific GPU architecture and TensorRT version, so they are not portable across heterogeneous fleets. That has to be part of the deployment design rather than discovered when a different instance type is provisioned.

Planning that properly is platform work assigned alongside [devops as a service](https://azendo.co/services/cloud-and-devops/).

## What we use TensorRT for.

* Throughput that reduces GPU count Optimised inference where hardware cost is the constraint.
* Precision validated after optimisation Accuracy measured post-quantisation on representative inputs.
* Engine builds matched to the fleet Architecture-specific compilation planned rather than discovered at deployment.

## How TensorRT capacity is assigned.

Inference optimisation is assigned inside AI capacity, with accuracy validation required after any precision reduction.

## Roles we assign TensorRT for

* [Computer Vision Engineer AI engineering](https://azendo.co/services/ai-engineering/computer-vision-engineer/)

## Service lines it sits in

* [AI engineering](https://azendo.co/services/ai-engineering/)

Capacity is agreed as a committed monthly capacity across a discipline, not per skill.

## Related in mlops and model delivery

* [Weights & Biases — skill we assign for](https://azendo.co/skills/weights-and-biases/)
* [Comet — skill we assign for](https://azendo.co/skills/comet/)
* [Neptune.ai — skill we assign for](https://azendo.co/skills/neptune-ai/)
* [DVC — skill we assign for](https://azendo.co/skills/dvc/)
* [Feast — skill we assign for](https://azendo.co/skills/feast/)
* [feature stores — skill we assign for](https://azendo.co/skills/feature-stores/)
* [Feature engineering — skill we assign for](https://azendo.co/skills/feature-engineering/)
* [cross-validation — skill we assign for](https://azendo.co/skills/cross-validation/)

## Tell us what your roadmap needs TensorRT for.

A service delivery manager replies with the disciplines we would assign, the monthly capacity and what the first month looks like.

[All skills we assign for](https://azendo.co/skills/)