---
title: "GPU inference | Skills We Assign For | Azendo"
description: "GPU inference in production — batching, utilisation, cost per prediction, plus the Azendo roles assigned for it."
url: "https://azendo.co/skills/gpu-inference/"
---

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

# GPU inference.

GPU inference serves model predictions on accelerator hardware, where throughput depends heavily on batching, memory management and keeping the device busy rather than on raw compute.

## Where GPU inference fits on a long engagement.

Serving one request at a time wastes most of a GPU. The hardware is built for parallelism, and a single small input leaves the device largely idle — which is why throughput on an unbatched service can be a small fraction of what the same hardware delivers batched.

Cost per prediction is the number that matters and is rarely calculated. A GPU instance billed hourly serving a low request rate can cost more per prediction than CPU inference would, and for many models the CPU option is entirely adequate.

## What an assigned team does with GPU inference.

Autoscaling GPU workloads is harder than autoscaling web services. Instances take minutes to become available and models take time to load, so scaling reacts far too slowly for a traffic spike.

Designing for that — warm pools, predictive scaling, queueing — is platform work assigned alongside [devops managed services](https://azendo.co/services/cloud-and-devops/).

## What we use GPU inference for.

* Batching that uses the hardware Requests grouped, because unbatched serving wastes most of the device.
* Cost per prediction calculated GPU compared against CPU honestly, since low volume often favours CPU.
* Scaling that anticipates rather than reacts Warm capacity, because instance start and model load are too slow to react.

## How GPU inference capacity is assigned.

Inference infrastructure is assigned across AI and platform capacity, with cost per prediction established before hardware is chosen.

## Roles we assign GPU inference for

* [Model Deployment Engineer MLOps engineering](https://azendo.co/services/mlops-engineering/model-deployment-engineer/)

## Service lines it sits in

* [MLOps engineering](https://azendo.co/services/mlops-engineering/)

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

## Related in mlops and model delivery

* [MLOps handoff — skill we assign for](https://azendo.co/skills/mlops-handoff/)
* [ONNX — skill we assign for](https://azendo.co/skills/onnx/)
* [TensorRT — skill we assign for](https://azendo.co/skills/tensorrt/)
* [Model registry — skill we assign for](https://azendo.co/skills/model-registry/)
* [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/)

## Tell us what your roadmap needs GPU inference 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/)