Low GPU Utilization
Software Workflow:
The application works by first identifying text from the audio in a video. It then translates this text into a target language and synthesizes a voiceover using the translated text. Finally, it merges the translated text, voiceover, and the original video into a new video. The GPU is heavily utilized primarily during the audio-to-text recognition phase, with minimal or no GPU usage in other stages.
GPU vs CPU: Principles and Differences
Imagine training a large AI model is like moving bricks.
The CPU is like an "all-rounder" – one person who can handle everything: calculations, logic, and management, no matter how complex. But it has a limited number of cores, at most a few dozen. Even if it's fast, it can only move a few, or at most a few dozen bricks at a time, making it inefficient.
A GPU, on the other hand, has a massive number of cores, often thousands or even tens of thousands. While each core can only move one brick, the sheer number of cores makes up for it! With thousands or tens of thousands of "helpers" working together, the bricks are moved quickly.
AI training and inference primarily involve "matrix operations" – essentially, a large number of numbers lined up to perform addition, subtraction, multiplication, and division. This is like having a massive pile of bricks waiting to be moved, a simple task that doesn't require much "brainpower".
The GPU's ability for "massive parallel processing" comes in handy here, allowing it to handle thousands or tens of thousands of small tasks simultaneously, making it dozens or even hundreds of times faster than a CPU.