CUDA

A development platform for NVIDIA GPUs. It is considered a key weapon in NVIDIA's dominance in the AI developer ecosystem.

CUDA is a development platform created by NVIDIA specifically for its GPUs. It is a set of dedicated tools that allow developers to easily use powerful calculation machines called GPUs, and can be likened to a dedicated app ecosystem that runs only on devices from specific companies.

It was created to enable GPUs, which were originally used for graphics, to be used for general-purpose calculations such as scientific calculations and AI learning. Thanks to its long-established library and developer community, it has become the de facto standard for AI development, and major AI frameworks run best on CUDA.

The problem is that CUDA only works on NVIDIA GPUs. As developers become more familiar with CUDA, a lock-in effect occurs that makes it more difficult to switch to another company's chip. This is a key weapon of NVIDIA's dominance and the reason why competitors are trying to create an alternative ecosystem.

✅ Why it matters

• This is a key concept in understanding why NVIDIA dominates the AI chip market
• High development productivity with tools and communities accumulated over a long period of time
• This is essential background knowledge for reading the competitive landscape of AI semiconductors

⚠️ Limits and debates

• It only works on NVIDIA GPUs, creating hardware dependence
• There is criticism that ecosystem lock-in gives NVIDIA the power to negotiate chip prices
• Alternative platforms are challenging, but it is not easy to narrow the ecosystem gap