[abra0]

I was thinking of doing something similar, but I am a bit sceptical about how the economics on this works out. On vast.ai renting a 3x3090 rig is $0.6/hour. The electricity price of operating this in e.g. Germany is somewhere about $0.05/hour. If the OP paid 1700 EUR for the cards, the breakeven point would be around (haha) 3090 hours in, or ~128 days, assuming non-stop usage. It's probably cool to do that if you have a specific goal in mind, but to tinker around with LLMs and for unfocused exploration I'd advise folks to just rent.

[imiric]

> On vast.ai renting a 3x3090 rig is $0.6/hour. The electricity price of operating this in e.g. Germany is somewhere about $0.05/hour.

Are you factoring in the varying power usage in that electricity price?

The electricity cost of operating locally will vary depending on the actual system usage. When idle, it should be much cheaper. Whereas in cloud hosts you pay the same price whether the system is in use or not.

Plus with cloud hosts reliability is not guaranteed. Especially with vast.ai, where you're renting other people's home infrastructure. You might get good bandwidth and availability on one host, but when that host disappears, you should hope that you did a backup, which vast.ai charges for separately, and if so, you need to spend time restoring the backup to another, hopefully equally reliable host, which can take hours depending on the amount of data and bandwidth.

I recently built an AI rig and went with 2x3090s, and am very happy with the setup. I evaluated vast.ai beforehand, and my local experience is much better, while my electricity bill is not much higher (also in EU).

[KeplerBoy]

Well rented cloud instances shouldn't idle in the first place.

[imiric]

Sure, but unless you're using them for training, the power usage for inference will vary a lot. And it's cumbersome to shutdown the instance while you're working on something else, and have to start it back up when you need to use it again. During that time, the vast.ai host could disappear.

[segmondy]

Most people don't think of storage costs and network bandwidth. I have about 2tb of local models. What's the cost of storing this in the cloud? If I decide not to store them in the cloud, I have to transfer them in anytime I want to run experiments. Build your own rig so you can run experiments daily. This is a budget rig and you can even build cheaper.

[isoprophlex]

Let me add that moving data in and out of vast.ai is extremely painful. I might be overprivileged with a 1000 MBit line but these vast.ai instances have highly variable bandwidth in my experience; plus even when advertising good speeds I'm sometimes doing transfers in the 10-100 KiB/s range.

[nightski]

Data as well. I have a 100TB NAS I can use for data storage and it was honesty pretty cheap overall.

[abra0]

Well if you are not using a rented machine during a period of time, you should release it.

Agreed on reliability and data transfer, that's a good point.

Out of curiosity, what do you use a 2x3090 rig for? Bulk not time-sensitive inference on down quanted models?

[imiric]

> Well if you are not using a rented machine during a period of time, you should release it.

If you're using them for inference, your usage pattern is unpredictable. I could spend hours between having to use it, or minutes. If you shut it down and release it, the host might be gone the next time you want to use it.

> what do you use a 2x3090 rig for? Bulk not time-sensitive inference on down quanted models?

Yeah. I can run 7B models unquantized, ~13-33B at q8, and ~70B at q4, at fairly acceptable speeds (>10tk/s).

[whimsicalism]

if you are just using it for inference, i think an appropriate comparison would just be like a together.ai endpoint or something - which allows you to scale up pretty immediately and likely is more economical as well.

[imiric]

Perhaps, but self-hosting is non-negotiable for me. It's much more flexible, gives me control of my data and privacy, and allows me to experiment and learn about how these systems work. Plus, like others mentioned, I can always use the GPUs for other purposes.

[whimsicalism]

to each their own. if you are having really high-sensitive conversations with your GAI that someone would bother snooping in your docker container, figuring out how you are doing inference, and then capturing it real-time - you have a different risk tolerance than me.

i do think that cloud GPUs can cover most of this experimentation/learning need.

[algo_trader]

together.ai is really good but there is a price mismatch for small models (a 1BN model is not x10 cheaper than 10BN models)

This is obviously because their are forced to use high memory cards.

Are there ideal cards for low memory (1-2BN) models? So higher flops/$ on crippled memory

[algo_trader]

> built an AI rig and went with 2x3090s,

Is there a goto card for low memory (1-2BN) models?

Something with much better flops/$ but purposely crippled with low memory.

[whimsicalism]

with runpod/vast, you can request a set amount of time - generally if I request from Western EU or North America the availability is fine on the week-to-month timescale.

fwiw I find runpod's vast clone significantly better than vast and there isn't really a price premium.

[mirekrusin]

For me "economics" are:

- if I have it locally, I'll play with it

- if not, I won't (especially with my data)

- if I have something ready for a long run I may or may not want to send it somewhere (it's not going to be on 3090s for sure if I send it)

- if I have requirement to have something public I'd probably go for per usage with ie [0].

[0] https://www.runpod.io/serverless-gpu

[kkielhofner]

With the current more-or-less dependency on CUDA and thus Nvidia hardware it's about making sure you actually have the hardware available consistently.

I've had VERY hit-miss results with Vast.ai and I'm convinced people are cheating their evaluation stuff because when the rubber meets the road it's very clear performance isn't what it's claimed to be. Then you still need to be able to actually get them...

[whimsicalism]

use runpod and yeah i think vast.ai has some scams, especially in the asian and eastern european nodes.

[wiradikusuma]

For me the economics is when I'm not using it to do AI stuff, I can use it to play games with max settings.

Unfortunately my CFO (a.k.a Wife) does not share the same understanding.

[ejb999]

I fear that someday I will die and my wife will sell off all my stuff for what I said I paid for it.

(not really, but it is a joke I read someplace and I think it applies to a lot of couples).

[segmondy]

Unless you are training, you never hit peak watts. When inferring, the watt is still minimal. I'm running inference now and using 20%. GPU 0 is using more because I have it as main GPU. Idle watt sits at about 5%.

Device 0 [NVIDIA GeForce RTX 3060] PCIe GEN 3@16x RX: 0.000 KiB/s TX: 55.66 MiB/s GPU 1837MHz MEM 7300MHz TEMP 43°C FAN 0% POW 43 / 170 W GPU[|| 5%] MEM[|||||||||||||||||||9.769Gi/12.000Gi]

Device 1 [Tesla P40] PCIe GEN 3@16x RX: 977.5 MiB/s TX: 52.73 MiB/s GPU 1303MHz MEM 3615MHz TEMP 22°C FAN N/A% POW 50 / 250 W GPU[||| 9%] MEM[||||||||||||||||||18.888Gi/24.000Gi]

Device 2 [Tesla P40] PCIe GEN 3@16x RX: 164.1 MiB/s TX: 310.5 MiB/s GPU 1303MHz MEM 3615MHz TEMP 32°C FAN N/A% POW 48 / 250 W GPU[|||| 11%] MEM[||||||||||||||||||18.966Gi/24.000Gi]

[KuriousCat]

When you compute the break even point did you factor in that you still own the cards and you can resell them? I bought my 3090s for 1000$ and after 1 year I think they go for more in the open market if I resell them now.

[ametrau]

Interesting. I checked it out. The providers running your docker container have access to all your data.

[lostmsu]

I just made a clone of diskprices.com for GPUs specifically for AI training, and it has a power and depreciation calculator: https://gpuprices.us

You can expect a GPU to last 5 years. So for 128 days break even you are only looking at 6.67% utilization. If you are doing training runs, I think you are going to beat it easily.

P.S. coincidentally or not, but shortly after it got mentioned on Hacker News, Best Buy run out of both RTX 4090s and RTX 4080s. They used to top the chart. Turns out at descent utilization they win due to the electricity costs.

[leobg]

Exactly. And you rarely see machines from Germany on vast. Might as well run a data center in Bermuda. [0]

[0] https://www.royalgazette.com/general/business/article/202307...

[cyanydeez]

the current economics is a low ball to get costumers. it's absolutely not going to be the market price once commercial interests have locked in their products.

but if you're just goofing around and not planning to create anything production worthy, it's a great deal.

[whimsicalism]

> the current economics is a low ball to get costumers.

vast.ai is basically a clearinghouse. they are not doing some VC subsidy thing

in general, community clouds are not suitable for commercial use.

[verticalscaler]

Well maybe you could rent it out to others for 256 days at $0.3/hour, tinker, and sell it for parts after you get bored with it. ;)

[Luc]

Breakeven point would be less than 128 days due to the (depreciating) resale value of the rig.

[segmondy]

Well, almost. GPUs have not be depreciating. The cost of 3090's and 4090's have gone up. Folks are selling it for what they paid for or even more. With the recent 40's SUPER series from Nvidia, I'm not expecting any new releases in a year. AMD & Intel still have ways to go before major adoption. Startups are buying up consumer cards. So I sadly expect prices to stay more or less the same.

[svnt]

If it isn’t depreciating that supports the parent’s bigger point even more.

[karolist]

He can use these cards for 128days non stop and re-sell, claiming back the purchase price almost fully since OP bought them cheap. Buying doesn't mean you use the GPUs to a point where they end up costing 0, yes there is risk with GPUs going but but c'mon.... Renting is money you will never see again.