The CUDA Ecosystem: 17 Years of Software Lock-in

CUDA is not just software; it’s a complete ecosystem that took 17 years to build.

The CUDA Software Stack

1. NVIDIA GPU Hardware: H100 → B100 → R100 → RTX Series
2. CUDA Driver API: Hardware interface
3. CUDA Runtime & Compiler: nvcc + PTX + CUDA C/C++ + Fortran
4. CUDA Libraries: cuDNN + cuBLAS + NCCL + TensorRT + Triton
5. Frameworks: PyTorch + TensorFlow + JAX + MXNet + PaddlePaddle
6. Applications: ChatGPT + Midjourney + Stable Diffusion + DALL-E + Copilot

The Numbers

• CUDA History: 2007 first release, 17 years head start
• Developer Base: 4M+ active developers
• AI Framework Share: ~95% (PyTorch + TensorFlow both CUDA-first)

The AlexNet Moment (2012)

AlexNet won ImageNet using CUDA. Result: CUDA became the AI lingua franca. Every major framework adopted CUDA-first.

Failed Alternatives

• AMD ROCm: Limited library coverage, years behind CUDA maturity
• Intel oneAPI: Cross-platform promise, minimal AI traction
• OpenCL: Open standard, performance gap vs CUDA

The Causality Dilemma

Why switching is nearly impossible:

1. Frameworks optimized for CUDA → Developers use NVIDIA
2. Developers on NVIDIA → Frameworks stay on CUDA
3. 17 years of compounding = insurmountable lead

The Lock-In in 3 Lines of Code

import torch
device = torch.device("cuda")  # NVIDIA only
model.to(device)  # Millions of codebases

Software Moat → Hardware Premium: NVIDIA sells an ecosystem that happens to require their chips.

This is part of a comprehensive analysis. Read the full analysis on The Business Engineer.