Nvidia’s grip on AI computing has long rested on two pillars: its hardware and its software. The GPUs get all the attention, but the real lock-in comes from CUDA, the proprietary programming platform that millions of developers have built their workflows around. OpenAI is now taking a direct shot at that second pillar.
The company is preparing to release a tool designed to let AI models run on non-Nvidia hardware, leveraging its open-source Triton language as a viable alternative to CUDA.
Triton’s quiet evolution
Triton isn’t new. OpenAI first released it back in July 2021 as an open-source language for writing high-performance GPU kernels in Python. The pitch was straightforward. CUDA is powerful but notoriously complex. Triton aims to deliver comparable performance with code that’s far more accessible to the average developer.
Since then, the project has been steadily gaining traction. It now serves as a backend for popular frameworks like PyTorch. The latest version, Triton 3.7, was released in 2026, signaling that OpenAI isn’t treating this as a side project.
