Nvidia's dominance in the AI computing space is largely attributed to several key factors that make it challenging for AMD to replace Nvidia GPUs in this domain:

1. **CUDA Ecosystem and Software Dominance**:

- **CUDA Platform**: Nvidia's CUDA (Compute Unified Device Architecture) is a proprietary platform that has become the standard in AI development. Major AI frameworks like TensorFlow and PyTorch are optimized for CUDA, making Nvidia GPUs the preferred choice for running AI models. This deep integration means that many AI applications are specifically designed to leverage Nvidia's hardware[1][3][4].

- **Software Ecosystem**: Nvidia has developed a comprehensive ecosystem around CUDA, including libraries such as cuDNN for deep learning and TensorRT for inference optimization. These libraries are highly optimized for Nvidia hardware and are not directly compatible with AMD GPUs, requiring significant redevelopment efforts for those wishing to switch[1][3].

2. **Tensor Cores and Specialized Hardware**:

- **Tensor Cores**: Nvidia GPUs are equipped with Tensor Cores, specialized hardware units that accelerate tensor operations crucial for deep learning tasks. These cores significantly enhance AI computation performance, especially in model training and inference[2][3].

- **AI-Specific Architectures**: Nvidia designs its GPUs with AI workloads in mind, incorporating optimizations and features like mixed-precision calculations that balance speed and accuracy in AI tasks. These features are less advanced or absent in AMD GPUs[1][2].

3. **Market Penetration and Industry Adoption**:

- **Widespread Adoption**: Nvidia GPUs are widely adopted across the AI industry, from research institutions to enterprises and cloud service providers. This entrenched market position creates a network effect, encouraging developers to use Nvidia hardware due to its widespread support and community backing[2][3].

- **Enterprise Solutions**: Nvidia offers enterprise-grade solutions like the DGX systems and the NVIDIA AI Enterprise suite, which are optimized for AI workloads. These systems are prevalent in data centers and among AI researchers, reinforcing Nvidia's dominance[1][2].

Overall, while AMD produces high-performance GPUs capable of handling AI workloads, Nvidia's established ecosystem, specialized hardware, and market penetration create significant barriers for AMD to directly replace Nvidia GPUs in the AI domain.

Citations:

[1] https://cacm.acm.org/opinion/nvidia-at-the-center-of-the-generative-ai-ecosystem-for-now/

[2] https://www.sesterce.com/blog/nvidia-ai-ecosystem

[3] https://developer.nvidia.com/blog/cuda-refresher-the-gpu-computing-ecosystem/

[4] https://weightythoughts.com/p/cuda-is-still-a-giant-moat-for-nvidia

[5] https://www.semianalysis.com/p/nvidiaopenaitritonpytorch