Cliff Weitzman

speaker

4,305 appearances 3 recordings 2 series first heard May 2026 last heard 5 Sep

Cliff Weitzman’s voice in public audio — every appearance, attributed to the second.

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recordings per month · last 12 months

Recordings per month over the last 12 months — 3 in all, peaking in May 2026 with 2.

Appearances

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The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch · 20VC: How to Build Your Own Data Center & Why Every Startup Should Do It | How ElevenLabs Leapfrogged Us: What I Learned | The AI Talent War: How Your Hiring Process Needs to Change with Cliff Weitzman, Speechify · 5 Sep 2026 podcast

Um, if I was to buy an H100 for let's say $30,000.

That's how much the kind of uh a single card would cost.

If I wanted to rent an H100 for one hour spot instance from GCP, it could cost me $5.

If I rented it from like, you know, Azure or AWS, maybe it'll cost me $3.5 per hour.

So if I multiply that times 24 hours and then times 365 days in a year, I'm actually gonna end up paying $35,000 to $50,000 to rent that GPU for one year.

But I could buy it for $30,000.

So it's 1.5x the cost of owning the hardware to rent the hardware for a year.

Now the hardware is typically warrantied for three years to work properly, but it'll keep working after the warranty for imagine, I don't know, 10 years.

So the math just maths where it makes way more sense to buy them.

The other big part is if you want to do large-scale training like we do, you need the memory to be co-located.

With a large cluster of GPUs.

I can't just rent from Google or Microsoft or even base 10 and run the size of training that I want because I need a gigantic memory card next to it with all of my data that all the GPUs are accessing.

So that's why we first started buying them.

The next thing that we found is actually if you run open source models for coding, you could pay anthropic.

You're paying for all the tokens and the fact that you're doing the branded, right?

Fable one.

Or you can run an open source model and instead of running it on a spot instance from Azure or anyone else, you run it on your own hardware and then you're paying a fraction of a fraction of a cent per token.

And so for all those reasons, it made a ton of sense, but we can go into all the depth that you want.

At Speechify, we still use K80s for a lot of specific operations for inference.

And we use older models of GPUs constantly.

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