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S1: A $6 R1 competitor?

(timkellogg.me)
851 points tkellogg | 8 comments | 05 Feb 25 11:05 UTC | HN request time: 0.994s | source | bottom
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swiftcoder ◴[05 Feb 25 13:25 UTC] No.42948127[source]▶
> having 10,000 H100s just means that you can do 625 times more experiments than s1 did

I think the ball is very much in their court to demonstrate they actually are using their massive compute in such a productive fashion. My BigTech experience would tend to suggest that frugality went out the window the day the valuation took off, and they are in fact just burning compute for little gain, because why not...

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gessha ◴[05 Feb 25 14:13 UTC] No.42948712[source]▶
This is pure speculation on my part but I think at some point a company's valuation became tied to how big their compute is so everybody jumped on the bandwagon.
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syntaxing ◴[05 Feb 25 14:24 UTC] No.42948854[source]▶
Matt Levine tangentially talked about this during his podcast this past Friday (or was it the one before?). It was a good way to value these companies according to their compute size since those chips are very valuable. At a minimum, the chips are an asset that acts as a collateral.
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jxdxbx ◴[05 Feb 25 14:42 UTC] No.42949098[source]▶
I hear this a lot, but what the hell. It's still computer chips. They depreciate. Short supply won't last forever. Hell, GPUs burn out. It seems like using ice sculptures as collateral, and then spring comes.
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1. sixothree ◴[05 Feb 25 15:05 UTC] No.42949424[source]▶
Year over year gains in computing continue to slow. I think we keep forgetting that when talking about these things as assets. The thing controlling their value is the supply which is tightly controlled like diamonds.
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2. adrianN ◴[05 Feb 25 15:09 UTC] No.42949510[source]▶
They have a fairly limited lifetime even if progress stands still.
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3. throwup238 ◴[05 Feb 25 15:55 UTC] No.42950318[source]▶
Last I checked AWS 1-year reserve pricing for an 8x H100 box more than pays for the capital cost of the whole box, power, and NVIDIA enterprise license, with thousands left over for profit. On demand pricing is even worse. For cloud providers these things pay for themselves quickly and print cash afterwards. Even the bargain basement $2/GPU/hour pays it off in under two years.
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4. sdenton4 ◴[05 Feb 25 18:08 UTC] No.42952661{3}[source]▶
Labor! You need it to turn the bill of sale into a data center and keep it running. The bargain basement would be even cheaper otherwise...
5. spamizbad ◴[05 Feb 25 18:11 UTC] No.42952694[source]▶
> Year over year gains in computing continue to slow.

This isn't true in the AI chip space (yet). And so much of this isn't just about compute but about the memory.

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6. ijidak ◴[05 Feb 25 18:15 UTC] No.42952765[source]▶
Honestly, I don't fully understand the reason for this shortage.

Isn't it because we insist on only using the latest nodes from a single company for manufacture?

I don't understand why we can't use older process nodes to boost overall GPU making capacity.

Can't we have tiers of GPU availability?

Why is Nvidia not diversifying aggressively to Samsung and Intel no matter the process node.

Can someone explain?

I've heard packaging is also a concern, but can't you get Intel to figure that out with a large enough commitment?

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7. eek2121 ◴[05 Feb 25 18:40 UTC] No.42953119[source]▶
From a per mm2 performance standpoint things absolutely have slowed considerably. Gains are primarily being eked out via process advantage (which has slowed down) and larger chips (which has an ever-shrinking limit depending on the tech used)

Chiplets have slowed the slowdown in AI, but you can see in the gaming space how much things have slowed to get an idea of what is coming for enterprise.

8. nl ◴[05 Feb 25 22:57 UTC] No.42956572[source]▶
> Isn't it because we insist on only using the latest nodes from a single company for manufacture?

TSMC was way ahead of anyone else introducing 5nm. There's a long lead time porting a chip to a new process from a different manufacturer.

> I don't understand why we can't use older process nodes to boost overall GPU making capacity.

> Can't we have tiers of GPU availability?

NVidia do this. You can get older GPUs, but more performance is better for performance sensitive applications like training or running LLMs.

Higher performance needs better manufacturing processes.