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PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #127
You work for a biotech startup that is experimenting with deep learning ML models based on properties of biological organisms. Your team frequently works on early-stage experiments with new…
The correct answer is D. A cluster with 4 n1-highcpu-96 machines, each with 96 vCPUs and 86 GB RAM. “writes custom TensorFlow ops in C++” -> use CPUs when “Models that contain many custom TensorFlow operations written in C++”: https://cloud.google.com/tpu/docs/intro-to- tpu#when_to_use_tpus
Submitted by fatima_kr· Apr 18, 2026ML model development
Question
You work for a biotech startup that is experimenting with deep learning ML models based on properties of biological organisms. Your team frequently works on early-stage experiments with new architectures of ML models, and writes custom TensorFlow ops in C++. You train your models on large datasets and large batch sizes. Your typical batch size has 1024 examples, and each example is about 1 MB in size. The average size of a network with all weights and embeddings is 20 GB. What hardware should you choose for your models?
Options
- AA cluster with 2 n1-highcpu-64 machines, each with 8 NVIDIA Tesla V100 GPUs (128 GB GPU
- BA cluster with 2 a2-megagpu-16g machines, each with 16 NVIDIA Tesla A100 GPUs (640 GB
- CA cluster with an n1-highcpu-64 machine with a v2-8 TPU and 64 GB RAM
- DA cluster with 4 n1-highcpu-96 machines, each with 96 vCPUs and 86 GB RAM
How the community answered
(37 responses)- A27% (10)
- B14% (5)
- C5% (2)
- D54% (20)
Explanation
“writes custom TensorFlow ops in C++” -> use CPUs when “Models that contain many custom TensorFlow operations written in C++”: https://cloud.google.com/tpu/docs/intro-to- tpu#when_to_use_tpus
Topics
#Hardware selection#Deep learning training#Custom TensorFlow ops#Resource allocation
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