Switching gears to training and inference devices, I’ve often fielded the question: “If I train my model on a GPU, can I run inference on a CPU? And what about the other way around?” The short answer is yes on both counts, but with a few caveats. Frameworks like PyTorch and TensorFlow serialize the model’s learned weights in a device‑agnostic format. That means when you load the checkpoint, you can map the parameters to CPU memory instead of GPU memory, and everything works—albeit more slowly. I’ve shipped models this way when I needed a lightweight on‑prem inference server that couldn’t accommodate a GPU but still wanted to leverage the same trained weights. Reversing the flow—training on CPU and inferring on GPU—is also straightforward, though training large models on CPU is famously glacial. Still, for smaller research prototypes or initial debugging, it’s convenient. Once you’ve trained your model on CPU, you can redeploy it to a GPU instance (or endpoint) by simply loading the checkpoint on a GPU‑backed environment. At AceCloud our managed inference endpoints let you choose the execution tier independently of how you trained: you can train on an on‑demand A100 cluster one day, then serve on a more cost‑effective T4 instance the next—without code changes. The end‑to‑end portability between CPU and GPU environments is part of what makes modern ML tooling so flexible, and it’s exactly why we built our platform to let you mix and match training and inference compute based on your evolving needs.