AMD's ROCm software stack crashes, hangs, or produces wrong results on 30% of PyTorch operations that work fine on CUDA

You buy an AMD Instinct MI250X (cheaper than H100, 128GB HBM2e) to train models. You install ROCm 6.0, set up PyTorch with ROCm backend, and run your training script. It crashes on torch.nn.functional.scaled_dot_product_attention with a cryptic HIP error. You find a GitHub issue from 6 months ago marked 'known issue, workaround: use math attention backend.' You apply the workaround. Training runs but is 40% slower than expected. You profile: flash attention is not working because the ROCm implementation has a memory leak on sequences >2048 tokens. You find another workaround. Training runs for 3 days then hangs at step 14,000 with no error message. The GPU shows 100% utilization but loss has stopped updating. You restart from checkpoint. It hangs at step 14,000 again. You spend 2 weeks debugging what would be a 'pip install torch && python train.py' on NVIDIA. So what? AMD GPUs are 20-40% cheaper than equivalent NVIDIA GPUs and often have more memory (MI250X: 128GB vs H100: 80GB). On paper, they should be the rational choice for budget-conscious AI teams. In practice, the ROCm software stack adds 2-10x the debugging time. Most teams try AMD, burn 2-4 weeks on compatibility issues, and switch back to NVIDIA. AMD's hardware is competitive; their software is 3-5 years behind CUDA. Why does this persist? NVIDIA has 5,000+ engineers working on CUDA/cuDNN. AMD has ~500 on ROCm. CUDA has 17 years of optimization. ROCm has 7. The gap is narrowing but PyTorch CI does not test ROCm as thoroughly — many edge cases only surface when real users run real workloads. AMD contributes to PyTorch but cannot match NVIDIA's upstream integration velocity.