Step-by-Step: How the GPU Actually Executes the AI Program

Let's walk through your exact scenario: Do we move instructions one by one, or move all 10,000 matrices first?

Here is the exact reality of how the GPU hardware processes this:

Step 1: The Boot Up (Loading VRAM)

Before the AI starts running, your computer's main CPU copies two things over the PCIe bus into the GPU's VRAM:

1. The Data: All the weights, biases, and matrix inputs of your AI model.
2. The Kernel Code: The compiled GPU binary instructions (the "AI assembly program").

Step 2: The Broadcast (The Instruction Fetch)

The GPU has a master block of hardware called the Instruction Scheduler. It reads one instruction from VRAM.
Let's say that instruction is: LOAD_VECTOR_REG (equivalent to your microcontroller's MOV or MVI).

Instead of sending this instruction to just one ALU, the scheduler broadcasts this single instruction to a block of 32 or 64 cores (called a Warp or a Wavefront).

Step 3: Parallel Register Loading (The Concept of Thread IDs)

You asked: Can we move 10,000 matrices into registers first?
We cannot fit all 10,000 at once because registers are limited. But we can load 32 or 64 of them in perfect unison.

How do they know which data to grab if they all get the exact same instruction?
Every individual core inside the GPU has a hardwired, unique number stamped on it called a Thread ID (e.g., Core 0, Core 1, Core 2...).

When the single broadcasted LOAD instruction fires, the compiler has written it mathematically using the Thread ID as an offset:

• Core 0 hears the instruction and loads: Base_Address + (0 * Matrix_Size)
• Core 1 hears the exact same instruction at the exact same time but loads: Base_Address + (1 * Matrix_Size)
• Core 2 loads: Base_Address + (2 * Matrix_Size)

In one single clock cycle, 64 completely different matrix segments fly out of VRAM and land into 64 different sets of registers across 64 different cores.

Step 4: The Math Trigger

Next, the Instruction Scheduler fetches the next instruction from VRAM: MATRIX_MULTIPLY.
It broadcasts this to all 64 cores. Every core executes the matrix math on the unique data sitting in its local registers. The results are calculated simultaneously.

Step 5: The Loop Repeats (Streaming)

Once those 64 matrices are done, the results are kicked back out to VRAM. The GPU increments its internal counters and fetches the next batch of 64 matrices out of the 10,000. It repeats this streaming loop until all 10,000 matrices are processed.