GPU vs CPU Performance for Matrix Multiplication

In this short experiment, I compared the matrix multiplication performance of my NVIDIA GeForce RTX 5070 Ti GPU with my AMD Ryzen 9 7900X CPU. The results were surprising — check it out!

Matrix multiplication is a fundamental operation in deep learning and scientific computing. It plays a central role in training neural networks, which is why GPUs have become essential tools in machine learning and AI research.

Method

I measured the time taken to multiply two large square matrices of size N×N on both CPU and GPU using MATLAB.

• CPU: AMD Ryzen 9 7900X
• GPU: NVIDIA RTX 5070 Ti
• Tool: MATLAB with built-in CPU and GPU matrix support

Results

The GPU significantly outperformed the CPU, and the performance gap grew as matrix size increased. For example:

• At size 1000×1000, the GPU was ~10× faster
• At size 10000×10000, the GPU was ~65× faster

This dramatic speedup is due to the GPU’s ability to perform highly parallel computations.

Conclusion

For large-scale matrix operations, especially in deep learning or numerical simulations, GPU acceleration offers orders of magnitude improvement over traditional CPUs. It’s not just a luxury — it’s a necessity for efficient computation.

Let me know if you’d like the exact benchmark code I used!