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by tedivm
1128 days ago
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I love the full stack deep learning crew, and took their course several years ago in Berkeley. I highly recommend it. One thing that always blows my mind is how much it is just not worth it to train LLMs in the cloud if you're a startup (and probably even less so for really large companies). Compared to 36 month reserved pricing the break even point was 8 months if you bought the hardware and rented out some racks at a colo, and that includes the on hands support. Having the dedicated hardware also meant that researchers were willing to experiment more when we weren't doing a planned training job, as it wouldn't pull from our budget. We spent a sizable chuck of our raise on that cluster but it was worth every penny. I will say that I would not put customer facing inference on prem at this point- the resiliency of the cloud normally offsets the pricing, and most inference can be done with cheaper hardware than training. For training though you can get away with a weaker SLA, and the cloud is always there if you really need to burst beyond what you've purchased. |
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That’s having it both ways, of course. You can’t both recoup the hardware cost in 8 months and have “free” downtime.
Under this pricing you need at least 25%ish duty cycle to break even (in 3 years) so probably still favoring buying, but for some people that might not add up. Pricing also varies drastically between providers, so this may depend on choice there.