Apr 22, 2026 · 41 min · 16 segments
You want to run AI locally. You have questions: What hardware do I actually need? Which framework should I use? How much will this cost? What's the realistic performance? In this episode, Sam brings…
So let's get straight to the big question that frames everything else.
The cloud versus on-prem debate is heating up.
What was the moment you real- realized running AI locally was not just viable, but actually preferable for certain use cases?
F- for me, there wasn't, like, one specific moment that made me want to pursue more of the on-prem, more of the local models as opposed to the cloud providers.
It really started as just sort of a general distrust with the, the cloud providers.
I was uncomfortable with taking my data sets and actually moving it to the cloud.
And, you know, there's plenty of evidence that there's other people that are struggling with this.
I saw a survey not long ago, ninety percent of its respondents were worried about the AI providers using their data without their consent, and that was sort of like the bubbling feeling inside of me is like, I don't know how this is gonna be used.
I'm not sure that it's smart for me to use these cloud providers in this way.
It was yesterday when Claude went down for hours, and that's not really a trust factor.
But it was just a few weeks ago that Claude had a major leak and some of their software got published to the internet completely by accident, and they had to go into damage control to do that.
You know, in, in my personal opinion, they may be the most trustworthy of the, of the model providers.
I think that he is a very sharp guy, and of all the players in this game, I trust him the most.
He is, you know, for any of your listeners that follow this to- sort of thing, know that he's within a hot seat with the Department of Defense.
You know, he was the one that set some hard lines on where, why, how he wanted his technology to be used and how he specifically did not want it to be used.
Um, and they felt like very reasonable hard lines, you know, like not mass surveillance on Americans, you know, as an example.
So let's get straight to the big question that frames everything else.
The cloud versus on-prem debate is heating up.
What was the moment you real- realized running AI locally was not just viable, but actually preferable for certain use cases?
F- for me, there wasn't, like, one specific moment that made me want to pursue more of the on-prem, more of the local models as opposed to the cloud providers.
It really started as just sort of a general distrust with the, the cloud providers.
I was uncomfortable with taking my data sets and actually moving it to the cloud.
And, you know, there's plenty of evidence that there's other people that are struggling with this.
I saw a survey not long ago, ninety percent of its respondents were worried about the AI providers using their data without their consent, and that was sort of like the bubbling feeling inside of me is like, I don't know how this is gonna be used.
I'm not sure that it's smart for me to use these cloud providers in this way.
It was yesterday when Claude went down for hours, and that's not really a trust factor.
But it was just a few weeks ago that Claude had a major leak and some of their software got published to the internet completely by accident, and they had to go into damage control to do that.
You know, in, in my personal opinion, they may be the most trustworthy of the, of the model providers.
I think that he is a very sharp guy, and of all the players in this game, I trust him the most.
He is, you know, for any of your listeners that follow this to- sort of thing, know that he's within a hot seat with the Department of Defense.
You know, he was the one that set some hard lines on where, why, how he wanted his technology to be used and how he specifically did not want it to be used.
Um, and they felt like very reasonable hard lines, you know, like not mass surveillance on Americans, you know, as an example.
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