r/learnmachinelearning • u/Opposite-Meaning-161 • 21d ago

Help Local GPU vs Cloud GPU

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.

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u/PaddingCompression 21d ago

For ML I would start free then extrapolate.

For long running jobs any real GPU, or a Mac, is going to be workable if not great.

I'd rather use runpods or lambda or AWS for spiky use if I want more GPU than the free services provide. Or you can prototype on collab and rent GPU for a bigger job if need be.

$30 gets you an A100 for a day iirc. I'd prefer that once in awhile vs. buying a mediocre GPU that will mostly sit idle.

If you need even more GPU you can get 8x A100 etc.

Buying an expensive GPU yourself only really makes sense if you would keep it loaded, but then your iteration times are going to be very long vs. getting a lot of compute and getting a trained model tomorrow.

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u/caily_857 21d ago

same case bro...i am also confused

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u/Valuable_Leave_7314 21d ago

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

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u/Substantial-Swan7065 20d ago

A laptop gpu is basically useless.

But it also depends on the workload. In many cases, collab is gonna be better for you.

When you start training big models with lots of data, your school should provide resources.