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JUI was asked this question in a Google Cloud interview. Most people default to GPU, but the answer depends on what you are building and how big it is.
1️⃣ Need to train a model with PyTorch or need high precision calculations (More decimal places, accurate)?
→ Go with GPU.
GPUs are general-purpose chips that work with all machine learning frameworks. They handle small to medium-sized models really well.
2️⃣ Training a massive TensorFlow model that takes days to train?
→Go with TPU.
TPUs are custom chips built specifically for TensorFlow operations. They trade some precision for incredible speed, and they are way more cost-effective when you are running huge training jobs.
In Google Cloud, this is Cloud TPU.
3️⃣ Still unsure?
→ Use GPU if you need flexibility or high precision. Use TPU only if you are using TensorFlow and training something really large. TPUs are not good for general math or non-TensorFlow work.
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👋Hi I’m Abhishek and I’m an AI Engineer at a Google Startup in Toronto.
The brutal truth: Companies need engineers who know how to pick the right hardware to scale.
🚀 To learn how I turned my ML skills into a Google Cloud ML Certification and got 5 AI job offers, comment “GPU” and I’ll send it to your DM.
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Keywords: #ai #feed #explore #reels #explorepage✨
@justsayabhi
