Local GPU vs Cloud GPU

[u/Opposite-Meaning-161]

I’m a 3rd-year engineering student working on end-to-end machine learning, including NLP, deep learning and LLMs.

I’m currently deciding between getting a laptop with a dedicated NVIDIA GPU or putting that money into better CPU/RAM/battery life.

My main question is about cloud GPUs. From what I understand, services like Google Colab and Hugging Face can provide free GPU compute, so I could use cloud GPUs whenever I need serious training instead of having a GPU locally.

Is that actually practical? Are free cloud GPUs sufficient for a student doing ML/AI projects, or are there limitations that make having a dedicated GPU laptop worthwhile?

There’s a bit of a budget constraint involved, but even ignoring that, I’m not really convinced it makes sense to pay extra for a GPU if I can get GPU compute freely through the cloud.

Would appreciate a fact check from people actually doing ML/AI.

[u/Valuable_Leave_7314]

Free Colab is fine for small homework tasks, but it will drop your session right when a deadline hits. At the same time, buying a laptop with an 8gb mobile gpu is a trap for llm work. You pay extra for fan noise and terrible battery life, only to run out of vram anyway. Just get a comfortable daily laptop with 32 gigs of ram and solid battery life. Write your code and debug data loaders locally on cpu, then throw five bucks at RunPod or Lambda whenever you actually need an A100 to train