Operate Real GPU Infrastructure

Launch, configure, connect to, and operate NVIDIA GPU environments on RunPod using practical Linux tools and workflows.

Deploy and Monitor GPU Workloads

Run PyTorch and AI workloads on real GPUs while monitoring utilization, VRAM, processes, performance, and system resources.

Debug, Secure, and Run Independently

Learn to diagnose GPU failures, secure remote access, and confidently operate temporary GPU infrastructure without relying on abstractions.

About the course

AICC’s partnership with Linuxademy.com brings practical, hands-on technical learning to AICC students, with a focus on understanding and operating the infrastructure behind modern AI systems. In this course, you will work with real NVIDIA GPU infrastructure on RunPod. You will launch GPU environments, run PyTorch and AI workloads, connect remotely with SSH, monitor GPU resources, diagnose failures, secure access, and operate GPU-powered applications. You will use technologies and tools including **Linux, RunPod, NVIDIA GPUs, CUDA, PyTorch, SSH, Hugging Face, and Linux monitoring tools**. You can follow along and build each project yourself, or simply watch the lessons to understand how GPU infrastructure works. Linuxademy’s mission is to help people become independent builders—able to **build, understand, inspect, debug, secure, and operate** the technologies they use.

Curriculum

3 Chapters · 7 Lessons

    1. (Included in full purchase)

      RunPod GPU Infrastructure Overview

    1. (Included in full purchase)

      Installing Ubuntu — WINDOWS + WSL2 + UBUNTU 24.04

    2. (Included in full purchase)

      Installing Ubuntu — APPLE MAC + UBUNTU 24.04

    1. (Included in full purchase)

      Before You Begin: Create a RunPod Account

    2. (Included in full purchase)

      Lesson 1: Launch RunPod GPU Environment

    3. (Included in full purchase)

      Lesson 2: Run A PyTorch Workload On The GPU

    4. (Included in full purchase)

      Lesson 3: Deploy An AI Model On The GPU

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