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Comment by jedberg | Hacker News Reader

I work for a company (Lambda Labs) that would span all these categories. We build GPUs and sell them, sending them off to the customer. We also build them and sell them, but then host it for the customer. We also buy them for ourselves and rent them either bare metal or virtualized.

The "build it and ship it to the customer" business is waning. The reason is because even the most deep pocketed customers no longer have data centers that can support the power and cooling needs of current GPUs. The latest from NVIDIA requires liquid cooling, tons of power, and solid floors (the racks are too heavy for raised floors).

Being able to run a modern server is becoming a very niche skill, one that most companies don't want or need to invest in.

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With the rise of WFH, I wonder if there could be a model (with some kind of trusted computing angle to prevent physical tampering) where you host your servers at employee locations/homes in a very distributed manner. Probably wouldn't fly with things like SOC2, but it's probably cheaper to pay/reimburse employees than pay for rack and network, especially with a small footprint.

The heat, noise, and space consumption would be unbearable, I think.

My garage (in Texas of all places) does fine

Not to say we're free of challenges... but hey. We can make this happen. I have a couple racks in the space where car doors are expected, yet can still park.

Think of the business continuity wins! Each employee has a Starting Google!

Reliability is one reason, residential internet and power is less reliable than commercial. You will have to provide UPS to everybody. Plus, if employees are located together then there is risk that regional power outage could take the system.

Then there is latency. Most apps talk to a data store, which is now separated from clients. The load balancers are going to be separated from the backend. Also, database servers tend to be beefy servers so either need to shared it or someone hosts a rack.

Another is that need write software to really be distributed. There is difference between distributed when things break and distributed all the time over the internet.

I wonder if that's less efficient though because now every employee has to manage a server, rather than having a small dedicated team that can manage hundreds or thousands of servers.

Just have a fleet of NUC like things (that have ECC RAM ones) that you ship around if they break. You could have them boot from network/CDN and never worry about imaging, too.

With 1Gb+ residential internet speeds, you could probably build a fairly robust and scalable platform like this.

If companies are seriously considering this, they should instead figure out how to distribute applications across their workstations to begin with, no need to distribute extra hardware if you can ensure security, connectivity, and uptime of a single laptop at each employees house.

Rack servers are significantly more powerful than NUC. This means going to need more little servers. And more employees or employees that host lots of servers and turn into point of failure. There are also powerful servers, like database, that can’t be split.

How many people do you think have symmetric gigabit? The company is going to have to pay for employees to upgrade. And do some separation to keep from saturating link from either work or home.

If there is access to the hardware, then the data and software are not secure. So you couldn't run any business applications or anything were you would need to have an assumption of data privacy.

This is ludicrous and I’d revolt. A desk is already too much space to give up to my workplace.

Do these solid floor racks still have troughs between the racks for flooding?

Datacenters I've been in that were built with "no raised floors" and flooding in mind do have a "raised floor", but it just happens to be solid concrete. This is very much the same as a warehouse or factory floor. It's built several feet higher than the surrounding parking lot, so that everything is level with loading docks.

If you're talking in-building flooding, not much going on there other than the racks themselves keep the equipment off the floor. All power is ran from overhead. A lot of modern "rack groups" (OCS, TSCIF, etc) have water limited water protection in mind.

Not that I've seen. It's just a concrete slab.

> The latest from NVIDIA requires liquid cooling, tons of power, and solid floors

We won't be running on GPUs forever. The innovation in the chip space will continue.

With as much margin as cloud takes, there will be increased interest in on-prem and mixed environments.

We run our own GPUs in production as well as having a cloud footprint.

GPUs are no longer GPUs... ai accelerators (AIA) is a better term. My MI300x don't have display ports. ;-)

I actually see a lot of parallels to bitcoin mining. It started on cpus, then gpus, then fpgas and soon after asics. We are starting to see something similar in the AI space with groq and tenstorrent.

The problem with asic's today is that in order to get to the same compute density of a AIA, due to AI's need for memory, you need significantly more hardware.

Right now, MI300x is the leader with 192gb, if you do the math on groq, it just doesn't work out. This is part of the reason why the stopped selling to retail and are just focused on hosting services.

It will be a while before the smaller chipmakers can get their hands on enough hbm to make a dent in the AIA market (due to AMD/NV hogging it all). Don't forget that AMD/NV can and will just come out with better designs too.

> We won't be running on GPUs forever. The innovation in the chip space will continue.

Sure, but I'm seeing a divergence. There are a group of chips that are getting smaller and require less power and cooling that can do a lot of workloads that you used to need huge chips for, but there are the most advanced chips for the most advanced workloads getting bigger and more power hungry.

There will always be the big customers who need the big chips, and that is what will be nearly impossible for them to self host.

This is the same argument that mainframe companies made in the 80s. Mass commodity systems will eventually kill the big gpu except for legacy and niche workloads. It's the tech infra circle of life.

I'm less optimistic. Unlike the 80s we are quickly approaching the limits of node feature sizes. Barring radically new designs and materials, I think it's reasonable to assume that we are reaching the power:computational limits of silicon.

Maybe in the case of CPUs. But, there is a fundamental technical shift going on away from computationally expensive things being done on CPUs and moving to AIAs. I suspect that this is why Jensen said Moore's law is dead.

Jensen said Moore's Law is dead because of the dichotomy explained a few times -- the free lunches are coming fewer and far between, you can't get a substantial performance boost and power decrease every 18 months, per Moore's Law.

We are reaching the power:computational limits of silicon.

Both GPU and CPU.

Here is the quote:

“Moore’s Law’s dead,” Huang said, referring to the standard that the number of transistors on a chip doubles every two years. “And the ability for Moore’s Law to deliver twice the performance at the same cost, or at the same performance, half the cost, every year and a half, is over. It’s completely over, and so the idea that a chip is going to go down in cost over time, unfortunately, is a story of the past. Computing is a not a chip problem, it’s a software and chip problem,” Huang said.

What we are seeing now are software engineers offloading computationally expensive workloads to AIA's, more and more. This is enabled through the use of libraries like PyTorch and access to HPC levels of compute that were not broadly accessible before.

The 'AIA's also have a scaling limit, and the coordination overhead increases exponentially too.

e.g. A million chickens could have a thousand times more muscle mass than two strong oxen, but I doubt anyone could plow a field even 2x faster with a million chickens.

So it's only a net benefit when the work can be split up into tiny parallel chunks and then recombined with near perfect efficiently.

> So it's only a net benefit when the work can be split up into tiny parallel chunks and then recombined with near perfect efficiently.

Hasn't that pretty much always been the case?

One thing we are seeing more and more of is composable fabrics where the PCIe bus is effectively being extended outside the case such that you log into a single instance, and instead of seeing just 8 AIA's, you now see 32+. This makes the coordination a lot easier.

I don't know how you keep rewording simple things everyone knows and are being patiently explained to you, as if you are encountering them the first time in the thread.

Which composable fabric am I talking about then?

>> So it's only a net benefit when the work can be split up into tiny parallel chunks and then recombined with near perfect efficiently. > Hasn't that pretty much always been the case?

I was talking about that, the fabric stuff is a non-sequitor, random hardware, doesn't make GPUs 2x in speed every 18 months, much less lead to a shift in everyday computing loads to GPUs.

1. Moore's Law also applies to GPUs.

2. If we could make use of 1000 cores for anything but long tail tasks, ye average CPU would have a lot more than 8 cores by now.

That's what the million chickens thing is about.

You can give me ultra-unobtianium fabric for "AIAs", it doesn't matter unless I have a algorithm that's massively parallelizable.

You seem stuck on the Moore's law of things and I've moved past that.

To be clear, I was responding to this from the OP above:

"and the coordination overhead increases exponentially too"

Cross node coordination is complicated and more easily managed with composable fabrics, which is why I brought it up.

I went back and read a bunch of your older comments here. At first, I thought it was personal but the tone that you use with me is carried across many comments that you've made to others. These also tend to be heavily downvoted.

I went and looked you up a bit more, it seems you had an exit to Google and then worked there for a number of years. There is something about how you're communicating that seems brilliant, but at the same time, is missing some level of EQ.

Like you're super smart, but also have some sort of ego about it all. As if you feel a deep need to somehow prove yourself as being "better" than the people around you. Saying things like this: "as if you are encountering them the first time in the thread"... is honestly, just rude.

My goal here is to help educate and discuss things as best as I know how. If that isn't good enough for you, honestly, just keep it to yourself. Unlike you, I'm not putting my ego into it. If you have a personal problem with me or my background or what I'm saying, my suggestion would be to stick with the technicals and leave the ego at the door.

Feel free to say whatever you want, but I won't respond to you any further.

I'm sorry, I'm just here to share knowledge.

There's a few things you got very wrong. I'm happy to talk about my background, but...I have a weird feeling you aren't actually interested.

Seems you thought I was putting you down personally so you needed to find out who I was. I don't know you from an 8 year old. You're brilliant too or whatever you need to hear.

Whole thing made me cringe. It's not worth it my friend. Makes you feel good until 30 seconds after you click post.

Going off on a long personal attack, claiming you did your OSINT research on your interlocutor, and they're victimizing you with their vicious claims that GPUs are limited by Moore's Law...not a good look.

> I'm sorry, I'm just here to share knowledge.

Apology accepted.

Update: Weird, now you keep going after I accepted your apology. Now you're editing your comments to change things around to somehow make yourself look better. ¯\_(ツ)_/¯

Also I flagged your comment, way out of bounds for HN. Also went back, lol, you got enraged because I didnt follow your rule I was only supposed to talk about the last sentence in the post you replied to. And now I remember why I was rolling my eyes and being direct with you. You were being rude to other people, kept changing the subject, and not addressing what people were saying. I.e. you completely ignored 2 different people's analogy about chickens, replied more about hardware, then I pointed out you did that, and you tried to pop quiz me, then when I patiently explained the relevance of their two comments, you went off about how you were done talking about anything else anyone said other than one sentence and I was trying to make myself feel smart (??? You're the one doing the pop quiz!!). Bonkers behavior.

What does AIA stand for? Is that short for "AI Assisted Chip Design" or something else?

AI Accelerator.

GPU is a poor fit for what an MI300x is, given that it doesn't really have anything to do with graphics any more. The boards don't even have display outputs.

CDNA3 vs. RDNA3

https://www.amd.com/en/technologies/rdna

https://www.amd.com/en/technologies/cdna.html

I think AI Accelerator

AI accelerator and it is 100% fine you don't, it's, uh, novel work.

The chickens won that decisively.

What's different today is the divide between training and inference. Inference is ridiculously cheap compared to training, and we're still early days with optimizations across the whole of the stack, so we'll have to see how it develops. Once constant training gets figured out, then we're really in for a ride.

The demand for compute utility capacity is effectively infinite. Eventually we'll move on from GPUs to some new architecture but the servers will still have to run in data centers that can provide enough power and cooling.

> With as much margin as cloud takes, there will be increased interest in on-prem and mixed environments.

Or cheaper clouds...

I wonder how much lock-in the current clouds actually have. And if they'll fully take customers down or entire industries hostage like the mainframes did.

GPU clouds use long term reserved cloud contracts as lock-in. Lambda started off kind of bragging about shorter term contracts on their website and then changed that to one year after the demand picked up (and investors piled in tons of money).

https://lambdalabs.com/blog/voltron-data-case-study-why-ml-t...

"While most other cloud companies offer contracts starting at one year, you can reserve a Lambda Reserved Cloud Cluster starting at six months."

Is any of this related to newer standards like Open Rack?

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