# Chap 2. Hardware

**Estimating your needs in the near future, evaluating the hardware options, and selecting the appropriate hardware ( the server, the data center, etc) are the basic skillsets for an MLOps developer.**

Hardware, HW bottlenecks, different types of workloads (memory intensive, compute intensive, memory bandwidth intensive). CPU vs GPU vs TPU. Tools to find the bottlenecks (is it IO/CPU compute/GPU compute/ GPU mem bandiwidth / GPU mem - CPU mem bandwidth). Scaling

It various much for a different scenario, for example, if you're a lab admin, startup's tech lead, or a solo performer,  

Resources in the reference:

cornell's lecture: <http://www.cs.cornell.edu/courses/cs6787/2017fa/Lecture11.pdf>

berkley's courses:&#x20;

<https://inst.eecs.berkeley.edu/~ee290-2/sp20/>

MIT's course:

{% embed url="<http://eyeriss.mit.edu/tutorial.html>" %}

Personal development: <https://www.mrdbourke.com/notes-on-building-a-deep-learning-pc/>

<https://blog.inten.to/hardware-for-deep-learning-current-state-and-trends-51c01ebbb6dc>


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