Understanding CUDA for AI Large Models: A Complete Visual Guide

CUDA (Compute Unified Device Architecture) is NVIDIA's GPU parallel computing platform and programming model that turns GPUs into general‑purpose processors. Modern AI large‑model training such as ChatGPT, DeepSeek, Claude, and Gemini all rely on the CUDA ecosystem.

CUDA Design Principle

The core idea is "CPU controls, GPU computes in parallel." The CPU excels at complex control logic and low‑latency tasks, while the GPU contains thousands of cores suited for highly parallel, repetitive work. CUDA’s fundamental concept is to split a massive task into millions of tiny tasks and hand them to the GPU.

Illustrative Example

for (i = 0; i < 100000000; i++) {
    c[i] = a[i] + b[i];
}

On a CPU this loop executes sequentially, processing one element after another (①②③④…). In contrast, a CUDA kernel launches hundreds of thousands of threads that perform the addition simultaneously, allowing all elements (①②③④…) to be processed at once.

Thus the GPU’s advantage is not raw clock speed but massive concurrent execution.

Typical CUDA Workflow

CPU prepares data and allocates memory.

Copy data from host memory to GPU device memory.

Launch a kernel, specifying grid and block configurations.

GPU executes the computation across many parallel threads.

Copy results from device memory back to host memory.

CPU performs any post‑processing or outputs the results.

This sequence embodies the "host control, device compute" model.