DEV Community

Cover image for How I built a cheap AI and Deep Learning Workstation quickly
Dmitry Noranovich
Dmitry Noranovich

Posted on • Edited on

How I built a cheap AI and Deep Learning Workstation quickly

This article discusses the process of building a workstation specifically designed for AI and deep learning, weighing both its benefits and potential drawbacks. The author explains the rationale for creating such a system, highlighting its advantages for those interested in the hardware side of AI, local development, or conducting research on a budget. Key technical considerations are covered, such as selecting a powerful GPU, a compatible CPU, and a motherboard that meets performance needs. Sufficient RAM, a spacious case for housing the GPU, and a robust power supply are also emphasized to ensure the system handles energy demands efficiently.

In addition to discussing component selection, the article examines the costs associated with high-end hardware like GPUs and the technical knowledge required to assemble a system. Although the author notes the availability of free resources like Google Colab and Kaggle, they suggest that building a workstation is advantageous for hands-on experience, local development, and budget-friendly, continuous research. The article concludes with a detailed look at component choices, covering GPUs, CPUs, motherboards, RAM, storage, power supplies, and considerations for multi-GPU setups. Drawing on their personal experience, the author shares their choice to use a refurbished PC and explains their selection process, offering practical advice for anyone considering building an AI workstation of their own.

Listen to the podcast version of the article part 1 and part 2 generated by NotebookLM. If you'd like to learn more, read my another article about why GPUs are used for Deep Learning and AI and check a searchable list of GPUs aggregated from Amazon, an app that I build in my spare time.

API Trace View

How I Cut 22.3 Seconds Off an API Call with Sentry 🕒

Struggling with slow API calls? Dan Mindru walks through how he used Sentry's new Trace View feature to shave off 22.3 seconds from an API call.

Get a practical walkthrough of how to identify bottlenecks, split tasks into multiple parallel tasks, identify slow AI model calls, and more.

Read more →

Top comments (0)

Billboard image

The Next Generation Developer Platform

Coherence is the first Platform-as-a-Service you can control. Unlike "black-box" platforms that are opinionated about the infra you can deploy, Coherence is powered by CNC, the open-source IaC framework, which offers limitless customization.

Learn more

👋 Kindness is contagious

Please leave a ❤️ or a friendly comment on this post if you found it helpful!

Okay