The most compelling reason is thatGPUs excel at parallel processing, which is ideal for handling large datasets and performing complex matrix operations(B). Let's explore this thoroughly:
* Parallel Processing Advantage: GPUs, like NVIDIA's A100, feature thousands of cores (e.g., 6912 CUDA cores, 432 Tensor Cores) designed for massive parallelism. AI tasks-especially matrix operations (e.g., dot products in neural networks) and data processing (e.g., batch computations)-are inherently parallelizable. For instance, multiplying a 1000x1000 matrix can be split across thousands of GPU threads, completing in a fraction of the time a CPU would take with its 4-64 cores.
* Use Case Fit: Large datasets require simultaneous processing of many data points (e.g., image batches), and complex matrix operations (e.g., convolutions) dominate deep learning. NVIDIA GPUs accelerate these via CUDA and Tensor Cores, offering 10-100x speedups over CPUs. Tools like RAPIDS further enhance dataset processing on GPUs.
* Real-World Impact: A startup needing high performance can't afford CPU bottlenecks; GPUs deliver the throughput to iterate quickly and scale efficiently.
Why not the other options?
* A (Larger caches): CPUs typically have larger per-core caches; GPU memory (e.g., HBM3) is high- bandwidth, not cache-focused, prioritizing throughput over latency.
* C (Single-thread performance): CPUs dominate here; GPUs trade single-thread speed for parallelism, irrelevant to this use case.
* D (Less power): GPUs consume more power (e.g., 400W for A100 vs. 150W for a high-end CPU) but offer vastly better performance-per-watt for parallel tasks.
NVIDIA's GPU architecture is built for this exact scenario (B).