Typically, software developers only support a single GPGPU API, and that API is nVidia CUDA.
Technically, consumer AMD cards are awesome at compute. For example, UE5 renders triangle meshes with compute instead of graphics https://www.youtube.com/watch?v=TMorJX3Nj6U Moreover, AMD cards often outperform nVidia equivalents because nVidia prioritized ray tracing and DLSS over compute power and memory bandwidth.
The issue is, no tech company is interested in adding D3D or Vulkan backend to AI libraries like PyTorch. nVidia is not doing that because they are happy with the status quo. Intel and AMD are not doing that because both hope to replace CUDA with their proprietary equivalents, instead of an open GPU API.
Typically, software developers only support a single GPGPU API, and that API is nVidia CUDA.
Technically, consumer AMD cards are awesome at compute. For example, UE5 renders triangle meshes with compute instead of graphics https://www.youtube.com/watch?v=TMorJX3Nj6U Moreover, AMD cards often outperform nVidia equivalents because nVidia prioritized ray tracing and DLSS over compute power and memory bandwidth.
The issue is, no tech company is interested in adding D3D or Vulkan backend to AI libraries like PyTorch. nVidia is not doing that because they are happy with the status quo. Intel and AMD are not doing that because both hope to replace CUDA with their proprietary equivalents, instead of an open GPU API.