This is what services like Vast.ai are for - super cheap GPUs you just use as long as you need etc etc.
Hmm, that looks interesting -- I might have to explore a bit. The low-end GPU pricing is pretty competitive.
Do you think that you can use those machines for confidential workflows for enterprise use? I'm currently struggling to balance running inference workloads on expensive AWS instances where I can trust that data remains private vs using more inexpensive platforms.
I read through the FAQ and the answer is "no", but they say it basically as "nobody really cares what your data is".
I wouldn't put anything confidential through it.
Re:We were wrong about GPUs
by gopher_space on Saturday February 15, 2025 @12:44AM
(#43056208)
What's your workload and timeline? I'm wondering how much of that workload could be handled in-house.
Just currently exploring how custom AI workflows (e.g. text to sql, custom report generation using private data) can help given the current SOTA. Looking to develop tooling over the next 3-6 months. I'd like to see what we can come up with before dropping $50-100k on hardware.
Re:We were wrong about GPUs
by gopher_space on Wednesday February 19, 2025 @02:16PM
(#43106154)
I threw together a toy project to see if it would help me understand the basic concepts and my takeaway was that, if you can shape your input into something a dedicated classification model (e.g. YOLO for document layout analysis) can work with, you can farm each class out to the most appropriate model.
It turns out that I can run most of the appropriate models on my ancient laptop if I don't mind waiting for the complicated ones to finish. If I do mind, I can just send that part to OpenAI or similar. If your workflow can scale horizontally like my OCR pipeline crap, every box in your shop with RAM >= 16GB might be useful.
Apologies if this is all stuff you're familiar with.
Of course you cannot use these machines "for confidential workflows for enterprise use", at least with AWS you know whose computer you're working with, but also keep in mind that it's really hard to steal your data as long as your data stays in memory, and you use something like mTLS to actually get it in and out of memory via E2EE. You can figure out the rest of your security model along the way, but anything sensitive (i.e. confidential) would surely fall way out of this model.
i use them a lot and constantly forget to turn mine off and it just drains my credits. i really need to write a job to turn them off when it's idle for longer than 20minutes
Re:We were wrong about GPUs
by everfrustrated on Friday February 14, 2025 @07:15PM
(#43054509)
GPU slices are absolutely a thing. Only supported on the enterprise GPUs tho and requires an additional paid Nvidia software license.
Re:We were wrong about GPUs
by andrewstuart on Friday February 14, 2025 @07:54PM
(#43054752)
Nvidia does not want slicing.
Eh, they have MIG.
Yeah, if you're running your own cluster.
Which you are! What ever happened to the MIG implementation work that y’all were working on? Last I heard it was “cursed” and nearly made someone go insane, which is very normal for NVIDIA hardware :)
No, I mean, if you're running your own cluster for yourself.
Re:We were wrong about GPUs
by loopholelabs on Friday February 14, 2025 @09:46PM
(#43055418)
I think time-slicing for GPUs is likely the solution here.
If you could checkpoint a GPU quickly enough it would be possible to run multiple isolated workloads on the same GPUs without any issues.
Nvidia vGPUs are time-sliced; MIG isn't. Neither work in arbitrary hypervised VMs.
Re:We were wrong about GPUs
by jacobyoder on Saturday February 15, 2025 @02:51PM
(#43061689)
vultr has fractional GPUs you can get as a VPS. I think I was paying about $55/month to test one out.
I don't know exactly what type of cloud offering would satisfy my needs, but what's funny is that attaching an AMD consumer GPU to a Raspberry Pi is probably the most economical approach for a lot of problems.
Maybe something like a system where I could hotplug a full GPU into a system for a reservation of a few minutes at a time and then unplug it and let it go back into a pool?
FWIW it's that there's a large number of ML-based workflows that I'd like to plug into progscrape.com, but it's been very difficult to find a model that works without breaking the hobby-project bank.