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CUDA vs ROCm on Linux: what matters for local AI hobbyists

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CUDA vs ROCm on Linux: What Matters for Local AI Hobbyists

As a local AI hobbyist with a budget of $800 for a build, you're probably looking to get started with machine learning and deep learning on your Linux workstation or homelab. But which GPU platform should you choose: NVIDIA's CUDA or AMD's ROCm? In this article, we'll dive into the details of each platform, highlighting their strengths and weaknesses, and provide practical steps for setting up a reliable and efficient local AI workflow.

Your Journey to Local AI

I remember when I lost my job in [year], it was a tough time. However, it sparked an interest in machine learning and deep learning that I hadn't explored before. With $800 as my budget, I've set out to build my own local AI workstation, using the skills I learned from online tutorials and YouTube channels.

Budget Breakdown

Let's assume you're building a basic AI workstation with the following components:

  • CPU: AMD Ryzen 5 5600X (~$299)
  • Motherboard: ASRock B450M Steel Legend Micro ATX
  • RAM: Corsair Vengeance LPX 16GB (2x8GB) DDR4 3200MHz
  • Storage: Samsung 860 EVO 1TB M.2 NVMe SSD (~$179)
  • GPU: NVIDIA GeForce RTX 3060 (~$300)

Hardware Pairs

To give you a more detailed look at what each platform has to offer, let's explore some popular hardware pairs:

  • CUDA:
    • AMD Radeon RX 5600 XT | NVIDIA GeForce GTX 1660 Super
  • ROCm:
    • AMD Radeon RX 5700 XT | NVIDIA GeForce RTX 2070

A Step-by-Step Guide

Here are the practical steps we'll take to set up a reliable and efficient local AI workflow using both CUDA and ROCm:

Step 1: Ubuntu Install

  • Open a terminal and run: sudo apt-get update && sudo apt-get install ubuntu-stretch
  • Follow the installation prompts to complete the installation.

Step 2: NVIDIA Drivers/CUDA Installation

  • Download the official NVIDIA drivers from the NVIDIA website.
  • Install the drivers using: sudo apt-get install nvidia-driver-440
  • Verify that the drivers are installed correctly by running nvidia-smi

Step 3: LM Studio Installation

  • Download the latest version of LM Studio from the LM Studio website.
  • Extract the archive using: tar -xvf lm-studio-1.0.4-1ubuntu0~20.04_amd64.deb
  • Follow the installation prompts to complete the setup.

Step 4: Golem Setup

  • Download the latest version of Golem from the Golem website.
  • Extract the archive using: tar -xvf golem-0.9.3-Linux-amd64.tar.gz
  • Follow the installation prompts to complete the setup.

5 Steps to Set Up CUDA vs ROCm on Linux

Here are the practical steps we'll take to set up a reliable and efficient local AI workflow:

Step 1: Hardware Pairs

Let's explore some popular hardware pairs for each platform:

  • CUDA:
    • AMD Radeon RX 5600 XT | NVIDIA GeForce GTX 1660 Super
  • ROCm:
    • AMD Radeon RX 5700 XT | NVIDIA GeForce RTX 2070

Step 2: Ubuntu Install (Repeat)

To ensure a smooth installation, let's repeat theUbuntu installation process:

  1. Open a terminal and run: sudo apt-get update && sudo apt-get install ubuntu-stretch
  2. Follow the installation prompts to complete the installation.

Step 3: NVIDIA Drivers/CUDA Installation (Repeat)

  • Download and install the NVIDIA drivers for CUDA.
  • Verify that the drivers are installed correctly by running nvidia-smi

Step 4: LM Studio Installation (Repeat)

  • Install LM Studio using the instructions above.

Step 5: Golem Setup (Repeat)

Follow the setup prompts to complete the installation of Golem.

Conclusion

In this article, we've explored the differences between CUDA and ROCm on Linux for local AI hobbyists. By following these steps, you'll be able to choose the best GPU platform for your needs and start building your own local AI workloads. As a local AI hobbyist with my own setup, I hope this guide has been helpful in getting you started.

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About the Author

As a local AI hobbyist with a budget of $800, I'm passionate about exploring the world of machine learning and deep learning. This article serves as a comprehensive guide to help you make an informed decision when choosing between CUDA and ROCm for your Linux workstation or homelab.

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