CUDA
Nvidia's parallel computing platform. The software layer that lets AI workloads run on Nvidia GPUs. Almost every serious AI model is trained and served through CUDA, which is why Nvidia has a structural moat in AI compute.
Also known as: Compute Unified Device Architecture
CUDA is the reason Nvidia dominates AI compute. It’s a parallel computing platform that Nvidia released in 2007, long before AI mattered to anyone outside research labs. By the time deep learning took off in the early 2010s, every major framework (TensorFlow, PyTorch, JAX) was being built on top of CUDA because Nvidia cards had a decade head start on the tooling. That head start compounded. Today, almost every AI model is trained on Nvidia hardware using CUDA, and the porting cost of moving to a different chip is the software, not the silicon.
The practical implication is that competitors like AMD (with ROCm), Intel, Huawei (with CANN and Ascend), and custom accelerators from Google (TPUs) and Amazon (Trainium) all have to either write their own CUDA-equivalent toolchain or translate CUDA workloads on the fly. Both approaches lose performance. Both approaches require teams of engineers. This is why benchmark parity on paper rarely translates into benchmark parity in deployment.
For anyone thinking about AI sovereignty, CUDA is a bigger dependency than the hardware itself. You can buy non-Nvidia chips, but if your software stack assumes CUDA, you’ve bought expensive paperweights. The ability of Chinese labs like DeepSeek and Moonshot to train frontier-grade models on Huawei’s Ascend line is a software achievement as much as a hardware one. Breaking the CUDA dependency is where hardware sovereignty actually starts.
In DeAI specifically, CUDA is why almost every decentralised inference network today runs on Nvidia. Alternative hardware paths exist on paper. In practice, they’re research projects, not production options.