You are completely mad if you are running any kind of valuable workload on Runpod community or Vast. Hosts are absolutely peeking inside the VM's (you can see it openly discussed in their Discords).
There's got to be a ton of valuable workloads that don't care much about secrecy.
Who cares if someones spying on the process of fine tuning a model to help us pick strawberries? Even if they could somehow find a competitor to sell it to - the competitor wouldn't be interested because the risk of violating trade secret law is surely greater than whatever they can glean from our training run...
Exactly. I've recently been training a number of models, and the value to any potential competitor would be approximately zero.
It's so integrated with the specific inputs of my product, and the outputs that it needs, that the model just has no value to anyone else.
If a competitor needed such a model, they need to train their own that matches the form of their inputs and outputs, which is just going to be different.
I hold the inverse risk assessment- I think acting on a peek risk is much higher with larger operations such as OpenAi or eg Amazon -as they've allegedly done the same on their product marketplace and deciding on generic products, in addition to open source-alike service offerings.
Larger entities have the resources, the inclination and the protective legal teams to act much differently than a resource tight n-tier platform operator
Large firms have a much harder time hiding that kind of behavior, and they have much more to lose.
Large firms require processes and controls around everything. If Amazon is peeking into corporate customers' data, then there have to be mechanisms designed to allow that, a whole bunch of people know, it's part of engineering designs, and a whistleblower is going to leak it to the press.
And then Amazon is going to lose all of its business customers once its privacy guarantees were shown to be intentionally circumvented.
It is simply not economically rational for a company like Amazon or OpenAI to look at the data of its corporate customers.
Regular consumers are a different story, because you need to respond to abuse, illegal activity, etc. So they don't have the same privacy guarantees.
But corporate customers, that stuff is watertight. No cloud provider is going to risk losing their entire business because they violated their privacy guarantees.
GPUs set to lower power states and CPUs with frequency scaling turned down. That's the biggest hit and miss for what I'm doing. Honestly don't care if people are looking at my workloads as it's just processing public data. But do care if it's someone's laptop that's actively using 1/2 the vram.
There's a lot of use cases that are not "commercially sensitive" that would require data center level privacy. E.g. a good percentage of academic research and experimentation, long running "boilerplate processing" for monitoring logs for e.g. a bird watching system or large scale web crawling where the individual website itself is mostly public. There are also use cases where the data itself is useless without other data to correlate. E.g. many log analytics use cases, if you are missing the main database data itself then the logs have zero value when intercepted. There are also use cases where you are doing general industrial level hotdog/not hotdog classification where unit economics is the most important and there's no real value to privacy e.g. on a factory floor (though sabotage is a bigger risk here).
You are ignoring the real usecases here. Not everything is large scale, secretly, b2b enterprise applications. Instead, many consumer applications are fully fine with being out in the open.
As an example, midjourney use to just publish ever generated image to their website.
If you are just doing inference things are public/open from the start, you dont have to worry much about the stuff you are talking about here.
You are completely mad if you are running any kind of valuable workload on Runpod community or Vast. Hosts are absolutely peeking inside the VM's (you can see it openly discussed in their Discords).
There's got to be a ton of valuable workloads that don't care much about secrecy.
Who cares if someones spying on the process of fine tuning a model to help us pick strawberries? Even if they could somehow find a competitor to sell it to - the competitor wouldn't be interested because the risk of violating trade secret law is surely greater than whatever they can glean from our training run...
Exactly. I've recently been training a number of models, and the value to any potential competitor would be approximately zero.
It's so integrated with the specific inputs of my product, and the outputs that it needs, that the model just has no value to anyone else.
If a competitor needed such a model, they need to train their own that matches the form of their inputs and outputs, which is just going to be different.
> Who cares if someones spying on the process of fine tuning a model to help us pick strawberries?
Privacy should be a default. It doesn’t matter what you’re doing.
It’s also unlikely they know what’s in the VMs until after they look at it.
> violating trade secret law
There is law for patents. Trade secrets exist outside the realm of enforcement.
Trade secrets are protected by civil (https://www.law.cornell.edu/uscode/text/18/1836) and criminal law (https://www.law.cornell.edu/uscode/text/18/1832). There's a history of enforcement actions under both.
Completely incorrect:
https://en.wikipedia.org/wiki/Trade_secret#United_States
> (you can see it openly discussed in their Discords).
Can you explain? I haven’t used Runpod and don’t have time to search through a Discord to figure out what someone is talking about.
I hold the inverse risk assessment- I think acting on a peek risk is much higher with larger operations such as OpenAi or eg Amazon -as they've allegedly done the same on their product marketplace and deciding on generic products, in addition to open source-alike service offerings.
Larger entities have the resources, the inclination and the protective legal teams to act much differently than a resource tight n-tier platform operator
It's really quite the opposite.
Large firms have a much harder time hiding that kind of behavior, and they have much more to lose.
Large firms require processes and controls around everything. If Amazon is peeking into corporate customers' data, then there have to be mechanisms designed to allow that, a whole bunch of people know, it's part of engineering designs, and a whistleblower is going to leak it to the press.
And then Amazon is going to lose all of its business customers once its privacy guarantees were shown to be intentionally circumvented.
It is simply not economically rational for a company like Amazon or OpenAI to look at the data of its corporate customers.
Regular consumers are a different story, because you need to respond to abuse, illegal activity, etc. So they don't have the same privacy guarantees.
But corporate customers, that stuff is watertight. No cloud provider is going to risk losing their entire business because they violated their privacy guarantees.
Could you please share screenshots?
A lot of Vast.ai GPUs are also firmware-limited, so it's a big gamble when you run your workloads. You also can't get a cluster.
It's fine for comfyui workloads or simple LLM inference, but you lose both privacy and stability.
GPUs set to lower power states and CPUs with frequency scaling turned down. That's the biggest hit and miss for what I'm doing. Honestly don't care if people are looking at my workloads as it's just processing public data. But do care if it's someone's laptop that's actively using 1/2 the vram.
There's a lot of use cases that are not "commercially sensitive" that would require data center level privacy. E.g. a good percentage of academic research and experimentation, long running "boilerplate processing" for monitoring logs for e.g. a bird watching system or large scale web crawling where the individual website itself is mostly public. There are also use cases where the data itself is useless without other data to correlate. E.g. many log analytics use cases, if you are missing the main database data itself then the logs have zero value when intercepted. There are also use cases where you are doing general industrial level hotdog/not hotdog classification where unit economics is the most important and there's no real value to privacy e.g. on a factory floor (though sabotage is a bigger risk here).
What are places in your view to rent GPUs that are trustworthy they will not peek in your opinion?
Sam is welcome to peek at my generative furry porn all he wants ( ͡° ͜ʖ ͡°)
( ͠° ͟ʖ ͠°)
You are ignoring the real usecases here. Not everything is large scale, secretly, b2b enterprise applications. Instead, many consumer applications are fully fine with being out in the open.
As an example, midjourney use to just publish ever generated image to their website.
If you are just doing inference things are public/open from the start, you dont have to worry much about the stuff you are talking about here.
There are a couple of new indices out there.
[1] "Silicon Index". Built against pricing observations, including listed prices and transaction data.
[2] "Ornn Compute Price Index (OCPI)". Claiming to use only executed transactions.
Your mileage may vary, but I find these new indices to be quite compelling.
[1] https://www.silicondata.com/products/silicon-index
[2] https://data.ornn.com/
And yup, its not just how much these resources cost, but how much people are willing to pay for them.
(edit: formatting)
> 4.57× between cheapest and dearest, for the same chip
> What the 4.57× is not
> - InfiniBand
> - A datacentre SLA
> - Enterprise compliance, committed capacity...
ok
Worth nothing that neither of those costs 2x the price of running GPU itself
Yeah but they have the infra and hence the leverage.
There’s much more data here, with more providers and splits by the different H100 types: https://cloud-gpus.com/nvidia-h100/
This is premise for https://skypilot.ai/, right? Cloud provider arbitrage.
Yes. And the prices a lot of times don’t even make sense
I’ve run into many hosted GPUs with processes stuck, with outdated drivers/libraries, or poor Internet connections
I wish there was some kind of GPU marketplace so one could just arb this opportunity.
I guess the author hasn't discovered vast.ai which is a minefield.
lol watch the $12 people just subcontract out to the $2 and hope no one notices lolll
But a ps5.48xlarge has 8x H100s. Are the other options here comparable?
Seems once the government controls it the prices go haywire
runpod is incredible, im a big fan, but good luck with availability on any specific GPU above 20gb of vram when you actually want to use it.
last time I was trying to run some workloads there the pricing of an h100 was largely irrelevant.
Same feeling. It’s great when GPUs are available. Also helps if you can use non data center cards in your workflow.
Hmm, if it works. Was interested so thought I would go check it out. Their site seems busted atm.
Slop