---
title: "CUDA | Skills We Assign For | Azendo"
description: "CUDA for GPU computing — version compatibility, memory management, plus the Azendo roles assigned for it."
url: "https://azendo.co/skills/cuda/"
---

[Skills](https://azendo.co/skills/) AI and machine learning 

# CUDA.

CUDA is NVIDIA's parallel computing platform for GPUs. It underlies every major deep learning framework, and its version compatibility with drivers and libraries is a recurring practical constraint.

## Where CUDA fits on a long engagement.

Most teams meet CUDA as a compatibility problem rather than a programming interface. Driver version, CUDA toolkit version, cuDNN version and framework build all have to agree, and a mismatch produces errors that do not describe the actual problem. Containers with pinned versions are the standard defence.

GPU memory is the constraint that shapes training work. Out-of-memory errors are the most common obstacle, and the responses — smaller batches, gradient accumulation, mixed precision, checkpointing — each trade compute or complexity for memory in different ways.

## What an assigned team does with CUDA.

GPU capacity is expensive and frequently underutilised. A training job using a fraction of available memory, or a serving deployment idling between requests, is spending at full rate for partial use.

Measuring and improving utilisation is straightforward work with direct cost impact, scoped alongside [devops managed services](https://azendo.co/services/cloud-and-devops/).

## What we use CUDA for.

* Environments pinned and containerised Driver, toolkit and framework versions fixed together, removing a whole class of failure.
* Training that fits available memory Mixed precision and accumulation applied deliberately rather than reducing the model.
* Utilisation measured GPU spend matched to actual use rather than paid at full rate for partial load.

## How CUDA capacity is assigned.

GPU work sits across AI and platform capacity, with environment reproducibility treated as a delivery requirement.

## Roles we assign CUDA 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 ai and machine learning

* [PyTorch — skill we assign for](https://azendo.co/skills/pytorch/)
* [TensorFlow — skill we assign for](https://azendo.co/skills/tensorflow/)
* [scikit-learn — skill we assign for](https://azendo.co/skills/scikit-learn/)
* [Pandas — skill we assign for](https://azendo.co/skills/pandas/)
* [NumPy — skill we assign for](https://azendo.co/skills/numpy/)
* [embeddings — skill we assign for](https://azendo.co/skills/embeddings/)
* [function calling — skill we assign for](https://azendo.co/skills/function-calling/)
* [vLLM — skill we assign for](https://azendo.co/skills/vllm/)

## Tell us what your roadmap needs CUDA 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/)