Author(s): MayhemCode

Originally published on Towards AI.

ROCm vs CUDA: Which One Should You Actually Use for AI?

I spent about three weeks last year trying to get a PyTorch model to train on an AMD GPU. I had the hardware, I had the code, I had the data. What I didn’t have was a working ROCm setup that didn’t randomly crash every four hours. I got it working eventually, but the whole experience taught me more about how GPU compute actually works than any tutorial ever did.

After explaining what CUDA and ROCm are (software layers that let GPU hardware run the massive matrix-multiplication workloads behind AI), the article argues that CUDA generally wins for training due to its mature ecosystem and long history of optimized libraries, while ROCm still lags in smoothness, driver/runtime issues, and library/inference tooling parity. It compares performance across data-center and consumer hardware, notes that cloud providers mostly default to NVIDIA (making CUDA the practical choice for most people), and concludes with guidance: choose CUDA for reliable production or large-scale training; consider ROCm if you want lower-cost hardware for learning or local inference where memory constraints matter; and step back to acknowledge that AMD is improving ROCm quickly, but CUDA remains the least-risk option right now.

Read the full blog for free on Medium.

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Published via Towards AI