---
title: "What are these \"GPUs\" really?"
description: "The post explains how graphics hardware evolved from CPU-offloaded video rendering into modern GPUs and why those devices now matter for AI/ML. It traces the path from the Commodore 64’s VIC-II and 3D accelerator cards through Quake, Tomb Raider, integrated graphics, and increasingly parallel hardware, while describing components such as framebuffers, command processors, shader hardware, memory, and display outputs. It then connects shader and CUDA cores to workloads including GPT-2, Stable Diffusion, and speech or text processing, and presents model chaining as more useful than treating models as isolated tools. A conference-video workflow combines ffmpeg, Whisper, subtitle segmentation, a large language model, Markdown, and human review to produce accessible written material. The conclusion distinguishes graphics-capable cards from A100 accelerators, which the post says cannot process graphics or encode video but are highly effective for AI/ML workloads."
---

# What are these "GPUs" really?

[Fly.io](https://yomu.fyi/company/fly-io) · Dec 11, 2023

**Type:** Explainer

## Summary

The post explains how graphics hardware evolved from CPU-offloaded video rendering into modern GPUs and why those devices now matter for AI/ML. It traces the path from the Commodore 64’s VIC-II and 3D accelerator cards through Quake, Tomb Raider, integrated graphics, and increasingly parallel hardware, while describing components such as framebuffers, command processors, shader hardware, memory, and display outputs. It then connects shader and CUDA cores to workloads including GPT-2, Stable Diffusion, and speech or text processing, and presents model chaining as more useful than treating models as isolated tools. A conference-video workflow combines ffmpeg, Whisper, subtitle segmentation, a large language model, Markdown, and human review to produce accessible written material. The conclusion distinguishes graphics-capable cards from A100 accelerators, which the post says cannot process graphics or encode video but are highly effective for AI/ML workloads.

## Context

The post addresses the evolution and capabilities of GPUs, including what they can and cannot do, as GPU hardware is being rolled out for AI/ML workloads near users.

## Approach / What changed

It explains GPU history and architecture through examples from early video hardware, 3D gaming, and modern accelerator components, then shows an AI/ML content-processing workflow using ffmpeg, Whisper, a large language model, Markdown, and human review.

## Takeaways

- The Commodore 64’s VIC-II demonstrated offloading video output tasks from the CPU to a dedicated device.
- Modern GPUs combine components including framebuffers, command processors, shader hardware, memory, display outputs, video encoders, raytracing cores, and AI/ML hardware.
- The post says A100 cards cannot process graphics or encode video, but are highly effective for AI/ML workloads and can perform more tasks per watt than CPUs.

**Tags:** [AI](https://yomu.fyi/topic/ai), [CUDA](https://yomu.fyi/topic/cuda), [GPU](https://yomu.fyi/topic/gpu), [Machine Learning](https://yomu.fyi/topic/machine-learning)

- Source: [Fly.io](https://fly.io/blog/what-are-these-gpus-really)
- Source URL: https://fly.io/blog/what-are-these-gpus-really
- Ingested by Yomu: 2026-09-03T01:23:42.668Z

[Read original post](https://fly.io/blog/what-are-these-gpus-really)