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Show HN: Oblivus GPU Cloud – Affordable and scalable GPU servers from $0.29/hr

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Re: Show HN: Oblivus GPU Cloud – Affordable and scalable GPU servers from $0.29/hr

#111

Post by dougSF70 »

crabbone wrote: post #109
dougSF70 wrote: post #88

Earlier quoted context omitted.

I have just bought 4 servers with 10 GPU (Titan X Pascal) and 384 GB of RAM for the price of a macbook. I found a solar powered CoLo facility to put them in. The economics of owning vs renting from AWS are worth it. My server set up is the same price as running one model on AWS.

If memory serves, NVidia's drivers can only be used for Tesla / Ampere family, if you are using them in a datacenter. Titan / GeForce are not allowed. So, there's that (but maybe I'm missing something). Now, "running" may also mean different things. For example, you may want to do things s.a. performance diagnostics (in order to understand if your code uses resources efficiently), and then you'd need stuff like NVML,…

I bought these from a Chinese internet firm. They dismantled their data center and I am buying it.

Re: Show HN: Oblivus GPU Cloud – Affordable and scalable GPU servers from $0.29/hr

#112

Post by crabbone »

cookieperson wrote: post #24
andrewstuart wrote: post #9

Lots of people seem to think they have no choice but to rent GPUs from cloud service providers. Remember retail GPUs are much more powerful, much cheaper and much more available than cloud GPUs. Just go to the store and buy one.

This is the actual answer. Why rent when you can own. The beefy GPUs of today will still carry their weight years into the future. If you take it even remotely seriously you'll save money and have resale value

> Why rent when you can own.

This is a very silly question... the answer is the same as with virtually anything else: real estate, transportation, wedding dresses...

I mean, you need to compare the price that will ultimately depend on the kind of thing you are doing... that's all. Sometimes it makes sense to rent, other times it makes sense to buy. There's no one right answer.

Re: Show HN: Oblivus GPU Cloud – Affordable and scalable GPU servers from $0.29/hr

#113

Post by dougSF70 »

dougSF70 wrote: post #111
crabbone wrote: post #109

Earlier quoted context omitted.

If memory serves, NVidia's drivers can only be used for Tesla / Ampere family, if you are using them in a datacenter. Titan / GeForce are not allowed. So, there's that (but maybe I'm missing something). Now, "running" may also mean different things. For example, you may want to do things s.a. performance diagnostics (in order to understand if your code uses resources efficiently), and then you'd need stuff like NVML,…

I bought these from a Chinese internet firm. They dismantled their data center and I am buying it.

Also, Datacenter GPUs only have passive cooling, presumably to allow for more Cuda cores. I get that what I have is older tech but for the cost of one H100 GPU I can have 60 of these servers (600 GPUs) plus some change to pay for CoLo fees.

Re: Show HN: Oblivus GPU Cloud – Affordable and scalable GPU servers from $0.29/hr

#114

Post by [deleted] »

dtx1 wrote: post #47
oblivuslimited wrote: post #46

Earlier quoted context omitted.

As someone whose native language is not English, I find it helpful to use AI to grammatically correct and improve my texts. There's no shame in asking for help or using tools to improve your language skills at all.

Same here and don't interpret it as an accusation, more of an "huh, that's interesting", mostly cause I had the suspicion that this was partly written by ChatGPT and was able to detect it, I'm not entirely sure what made me realize it.

[deleted]

Re: Show HN: Oblivus GPU Cloud – Affordable and scalable GPU servers from $0.29/hr

#115

Post by crabbone »

dougSF70 wrote: post #113
dougSF70 wrote: post #111

Earlier quoted context omitted.

I bought these from a Chinese internet firm. They dismantled their data center and I am buying it.

Also, Datacenter GPUs only have passive cooling, presumably to allow for more Cuda cores. I get that what I have is older tech but for the cost of one H100 GPU I can have 60 of these servers (600 GPUs) plus some change to pay for CoLo fees.

I'm not trying to discourage, and I don't have concrete numbers on hand, but there are other factors, beside the price of h/w. Like, obviously, electricity use, data locality / moving it around (with more smaller units you'd have to move it more, also, not sure if consumer-grade GPUs support NVLink, and even if they do, then at what bandwidth?)

For some of these, you could obviously pay with your time. Sometimes that time is very valuable, and sometimes you have a lot to spare.

Also, the amount of VRAM (3x)... Sometimes having too little of it means having to re-write the program, or it could dramatically impact the speed. Similarly for bandwidth (8x). But, again, if the kind of workload you have isn't constrained by either, then you could probably win by running on more smaller / older GPUs.

Re: Show HN: Oblivus GPU Cloud – Affordable and scalable GPU servers from $0.29/hr

#117

Post by cookieperson »

circuit10 wrote: post #27
cookieperson wrote: post #24

Earlier quoted context omitted.

This is the actual answer. Why rent when you can own. The beefy GPUs of today will still carry their weight years into the future. If you take it even remotely seriously you'll save money and have resale value

Because you’ll probably be only be using it for a small fraction of the time you have it, so sharing can be much more cost effective depending on how much you use it

I dropped a couple hundred bucks on a GPU years ago. It's done a lot of work for me and has years to go. True if you don't plan on using it no point in buying it. Same goes for literally anything

Re: Show HN: Oblivus GPU Cloud – Affordable and scalable GPU servers from $0.29/hr

#118

Post by iJohnDoe »

dougSF70 wrote: post #88
oblivuslimited wrote: post #11

Earlier quoted context omitted.

Thank you for sharing your thoughts. Acquiring the latest technology, such as an A100 GPU, individually from a store can indeed be quite expensive, with prices around $10,000 per unit. Additionally, setting up and maintaining a home lab to scale from one GPU to thousands can be a significant investment in terms of both cost and resources. In contrast, our platform provides access to the latest GPU technology without…

I have just bought 4 servers with 10 GPU (Titan X Pascal) and 384 GB of RAM for the price of a macbook. I found a solar powered CoLo facility to put them in. The economics of owning vs renting from AWS are worth it. My server set up is the same price as running one model on AWS.

Can you please share where to buy this from? I would like to purchase one. Thanks!

Re: Show HN: Oblivus GPU Cloud – Affordable and scalable GPU servers from $0.29/hr

#119

Post by _boffin_ »

dougSF70 wrote: post #88
oblivuslimited wrote: post #11

Earlier quoted context omitted.

Thank you for sharing your thoughts. Acquiring the latest technology, such as an A100 GPU, individually from a store can indeed be quite expensive, with prices around $10,000 per unit. Additionally, setting up and maintaining a home lab to scale from one GPU to thousands can be a significant investment in terms of both cost and resources. In contrast, our platform provides access to the latest GPU technology without…

I have just bought 4 servers with 10 GPU (Titan X Pascal) and 384 GB of RAM for the price of a macbook. I found a solar powered CoLo facility to put them in. The economics of owning vs renting from AWS are worth it. My server set up is the same price as running one model on AWS.

Uhh… can you share details… this seems like price point of things when they’ve fallen off the back of a truck.

Re: Show HN: Oblivus GPU Cloud – Affordable and scalable GPU servers from $0.29/hr

#120

Post by etherael »

upbeat_general wrote: post #91
etherael wrote: post #22

Earlier quoted context omitted.

Even so, an A100 is not actually faster than an RTX 4090, right? So basically the reason you would want to use something like this instead is because you need the VRAM?

That’s not the case. It is significantly faster, at least for deep learning workloads. Check out Lambda Lab’s benchmarks.

https://lambdalabs.com/gpu-benchmarks for those who are curious, ends up being quite the difference no matter which way you slice it, up to 60% better. Of course, this is probably due to the increased interconnect bandwidth and memory rather than raw compute horsepower, but for the workloads in question that's relevant.
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