Moving beyond hardware specifications

While this post is specifically related to consumer hardware, it has implications for AI applications, in general.

First the context:

The Danger of Focusing on Specs – Purism

While my Windows-using colleagues were replacing computers every two or three years as they grew slower and slower with age, I found my Linux-using friends and myself were often using the same hardware (even second-hand hardware) for at least twice as long. Even when I replaced hardware with something new, I found that the old hardware still performed, for the most part, as well as it did when I started using it. The hardware specs didn’t matter nearly as much as the software that ran on it.

I agree. I had mentioned before that Linux Mint gave my old Mac Air a completely new lease of life. The battery is failing, but I keep it connected to the mains. I belatedly realised that an iPad Pro is the worst performer for any “computing needs ” despite the sticker price.

Nevertheless, these issues will assume significant importance for AI modelling (whether cloud or on-premises), as enterprises shift towards it and get locked into specific vendors (with significant egress fees). I had never realised this until I got locked in the Dropbox ecosystem. I “rely” on cloud backups and sync, and Dropbox has a shitty front-end for Linux without offering a similar “experience” on Linux as on Windows. I am happy to report that it is a major performance lag on Mac!

Jokes (and a little sarcasm) apart, the key takeaways are:

Here are a few specs you should focus on:

AI models will “evolve” rapidly, and therefore, there has to be some system without the hardware upgrade cycle “kicks in”.

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