Posted by bmulholland 17 hours ago
> However, as previously mentioned, Jalapeño’s results are obtained without speculative decoding and Vera Rubin’s results use speculative decoding. Speculative decoding leads to a ~3-5x reduction in cost per token. When speculative decoding is implemented on Jalapeño, this will enable Jalapeño to serve tokens even more cost effectively.
How much speculative decoding improves throughput is workload-dependent. Yes, it can improve performance by 5x, but it can also slow down performance by 2x. It also requires a vastly more complex decode loop than the standard token-by-token decode. The tech journos didn't ask themselves if speculative decoding improves perf so much why wasn't it on by default? Perhaps, because speculative decoding is not optimal for OpenAI's ASIC? Perhaps that is also why they were only able to benchmark the not-very-representative single-user-decode case?
They also fawn over the chip’s TDP when all other chips have to support 16 bit floating point and thus must run much hotter.
They make the classic mistake of equating max TDP with in-use-watts, and praise this magnificent (fictitious) performance per watt at FP8 with other chips’ max-TDP at FP16, which draw twice the power.
Evidence that the IPO can’t be far away.
I went down a rabbit hole after watching Dylan Patel on Dwarkesh today: https://www.youtube.com/watch?v=aV26V1UvkJw
I was initially just surprised by how bullish Dylan is on OpenAI/Anthropic and how bearish he is on China, despite Chinese labs getting closer to US SOTA while offering inference at dramatically lower prices.
So, I started digging while waiting for various day-job inference calls to return, ha.
Dylan says he spent years obsessively posting on hardware forums, moderating hardware subreddits, and running anonymous hardware blogs/videos before SemiAnalysis. But he also says most of that history is now gone, including from the Internet Archive, because he asked for it to be removed.[1]
In a 2024 interview he described his post-college job as “data science” around hurricane/earthquake/wildfire simulations for a financial company.[1] In a 2026 Sequoia interview he described himself as having been a “quant at a small quant risk firm” who generated $10M+ of “risk-free revenue.”[2] The Information reports that he declined to identify the employer and doesn’t list it on LinkedIn.[3]
Even harmless/silly stuff seems to drift. In February he said he kept bees for ~1.5 years. Today it was “few months, few months.”[4][5] I know, sort of silly and doesn't matter.
The Information reports that Patel owns stakes in ~20 startups in the same ecosystem SemiAnalysis covers, organized a $50M Fluidstack SPV, and is now targeting a $400M venture fund.[3][6]
And, in a 2022 HN discussion about SemiAnalysis disclosures, after saying his reports had moved smaller stocks by 20% in a day, Patel wrote: “If I thought I could move the stock, I'd make the position in the morning alongside my clients, and publish shortly after.”[7]
I don’t know that any of this is false or that anything improper happened (I’m definitely not claiming that). More that 1-2 of these things would just be odd. Taken together, though, they made me question how much trust I was putting in the broader story.
The dynamic of reminds me of crypto, WeWork, Theranos, Citron, etc. Once enough important people validate someone, things that would normally invite basic diligence somehow stop getting questioned.
[1] https://www.dwarkesh.com/p/dylan-jon
[2] https://sequoiacap.com/podcast/dylan-patel-of-semianalysis-w...
[3] https://www.theinformation.com/articles/dylan-patel-semianal...
[4] https://www.latent.space/p/dylanpatel-cooking
[5] https://www.dwarkesh.com/p/dylan-patel-3
[6] https://www.theinformation.com/briefings/exclusive-semianaly...
The other side of the conversation is the idea that large models in DCs on custom silicon is the future. Maybe for enterprise? But consumers will eventually (10 yrs) have affordable hardware designed to run crazy-good local models (more RAM + higher bandwidth). That will take pressure off of datacenters, but also reduce AI profits, and move that money to consumer chip/device makers. Apple is once again the biggest winner. Nvidia consumer chips might get cheaper, but nerfed, to encourage datacenter use where they make more money. I'm hoping AMD can stop being terrible at software so that when we finally have their better hardware we can actually use it.
Example of such a system being used specifically for datacenters: https://blog.vantage-dc.com/2026/04/22/cooling-without-the-d...
Evaporated water is condensed, and in the process transfers its heat into another place that removes it. Another simple example is a pot of boiling water with a lid on it.
- If they use either evaporative cooling or a liquid-cooled heat exchanger, that uses tons of water consistently. This requires less energy (it's mostly passive) so you use more water.
- If they use closed-loop water cooling and/or heat pumps/electric chillers, that uses much less water - at the DC. But it does require more energy to circulate the water, run fans, etc. If you are using more energy, where is the energy coming from? It's coming from power plants, which require... you guessed it... more water (e.g. thermoelectric, hydroelectric, geothermal, concentrated solar). They need water in order to generate the power, and lots of it. Coal, natural gas, nuclear, and concentrated solar, all use steam to generate energy. Nuclear also uses water to cool the reactor. And water is used extensively to extract coal, oil, and natural gas. Geothermal uses water in the ground.
You can't not use a ton of water in one fashion or another. It just depends what method, and on what end the water is used. And the crazy thing is, most new datacenters are being built in places with extremely little water. Guess how that's gonna work out as the planet gets hotter?
I don't know why I got downvoted to hell for stating facts every datacenter architect knows. HN be HN'in.
One objective of the project might be simply to provide credible negotiating leverage when dealing with existing suppliers like NVidia. You don't have to deploy at scale for that to work, but you do have to look like you could if pushed hard enough.
But then that means you have no actual moat against the behemot, right? Your competitor can move into the market as soon as they want to, at much better cost (so at slightly better price)... and Nvidia certainly can adapt much faster around hard hardware specs innovation than a new entrant ever could.
One answer is they're quite good at poaching talent.
They should GTFO with this crap.
Create barriers to computing for ordinary people while milking businesses for tokens.
There's a reason why Micron and Nvidia are the crown jewels of American technology right now and for the foreseeable future.
To give some context, Intel started making DRAM, I think they were actually the company that came up with modern memory techniques. They exited the market and pursued a more lucrative moat with CPUs.
If somehow you get around the patent issues, you're now faced with huge research and development costs, fabs to build, processes to sort out and all of that has very high failure rates.
Last time I checked Micron was the largest patent holder in the world and even for them this is a hard area where they are number 3 in the market.
If the rich decided to buy all drinking water, you would probably be saying that's okay, making water is difficult, shortly before dying.
I remember when Tesla was building its own inference chips, and after about 2 years and billions spent, the whole effort was scuttled b/c they simply could not keep up with the iteration and R&D cycles of dedicated chip companies. I suspect the same will be the case with OpenAI vs Cerebras + Nvidia/Groq
Even in nvidia land rubin + LPU does a similar thing.
It has its downsides of course - if your traffic swings prefill heavy to decode heavy, you can't suddenly use your lpu for prefill. With GPUs they're totally interchangeable. Tradeoffs.
Hes got a press release.
The issue is, baking something to silicon requires discipline and about 2 years.
This isn't something you can just change your mind on halfway through. Trust me, I know. You need a clear vision of what you want to support, why and what bits of a chip you need to achieve that.
Man, if only someone made like, chips that could lots of different calculations all at the same time!
That sounds quite like...nonsense?
Chip companies work on years-long cycles. They know today what are they launching 4-5 years from now.