GPU Utilization Too Low
How the software works:
The process involves recognizing text from audio in a video, translating the text into a target language, synthesizing voiceover in the target language, and merging the text, voiceover, and video into a new video. Only the speech-to-text recognition stage heavily utilizes the GPU, while other stages use little to no GPU.
GPU vs CPU: Principles and Differences
Imagine training an AI model is like moving bricks.
The CPU is like an "all-rounder" that can handle many tasks: computing, logic, and management, no matter how complex. It excels at everything but has a limited number of cores, typically up to a few dozen.
Even if it moves bricks quickly, it can only handle a few or at most dozens at a time, making it inefficient and exhausting.
On the other hand, the GPU has a staggering number of cores—often thousands or tens of thousands. Although each core can only move one brick at a time, the sheer number of cores makes up for it. With thousands of "helpers" working together, the bricks are moved in no time.
The core task of AI training and inference is "matrix operations"—essentially, a large number of numbers performing addition, subtraction, multiplication, and division in sequence. It's like moving massive piles of red bricks, a simple task that requires no complex thinking, just repetitive work.
The GPU's "massive parallel processing" capability is perfectly suited for this, allowing it to handle thousands or tens of thousands of small tasks simultaneously, making it dozens or even hundreds of times faster than the CPU.
The CPU, however, is better suited for sequential and complex tasks, such as playing a single-player game or writing a document. When it comes to AI's massive "brick-moving" workload, the CPU can only handle a few or dozens at a time, struggling to keep up with the GPU.