AI hardware platform support

AI applications must properly handle different GPU platforms (NVIDIA CUDA, AMD ROCm, Iluvatar Corex, etc.) through both build-time configuration and runtime capability detection. This ensures models can run efficiently across diverse hardware environments.

Build-time considerations:

• Use parameterized builds to support different PyTorch variants for target platforms
• Configure appropriate package indices for platform-specific dependencies

Runtime considerations:

• Implement capability checks before using platform-specific features
• Provide graceful fallbacks when advanced features aren’t supported
• Add platform detection logic to automatically adjust behavior

Example implementation:

# Runtime capability check
def cuda_malloc_supported():
    # Check if platform supports cudaMallocAsync
    if platform_is_iluvatar():
        return False
    return True

# Build-time configuration
# docker build --build-arg VARIANT=cu126 .  # NVIDIA
# docker build --build-arg VARIANT=rocm6.2 .  # AMD
# python main.py --disable-cuda-malloc  # Iluvatar fallback

This approach ensures AI applications remain portable and performant across different hardware platforms while leveraging platform-specific optimizations when available.