Low GPU Usage
How the Software Works:
The software identifies speech in a video, translates the identified text to a target language, synthesizes a voiceover in the target language, and then merges the text, voiceover, and video into a new video. Critically, heavy GPU usage is primarily concentrated during the speech-to-text transcription phase. The remaining stages of the process either do not utilize the GPU or require minimal GPU resources.
GPU vs CPU: Principles and Differences
Imagine training a large AI model is like moving bricks.
A CPU is like an "all-around player," capable of handling various tasks: calculations, logic, and management, no matter how complex. However, it has a limited number of cores, usually only 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, there are so many of them! Thousands of cores working together can quickly move a mountain of bricks.
AI training and inference primarily involve "matrix operations"—essentially a large number of numerical calculations (addition, subtraction, multiplication, and division). It's like moving a huge pile of bricks - a simple task that doesn't require much "thinking".
GPU's "massive parallel processing" capability is perfectly suited for this. It can handle thousands or even tens of thousands of small tasks simultaneously, making it tens or even hundreds of times faster than a CPU.