I'm not in a position to recommend or not a particular provider for gpu-equipped servers, simply because I've never had the need for gpus.

My first thought was related to colocation services. From what I understand, a lot of people avoid on-premise/in-house solutions because they don't want to deal with server rooms, redundant power, redundant networks, etc.

So people go to the cloud and pay horrendous prices there.

Why not take a middle path? Build your own custom server with your perferred hardware and put in a colocation

There are several tier-two clouds that offer GPUs but I think they generally fall prey to the many of the same issues you'll find with AWS. There is a new generation of accelerator native clouds e.g. Paperspace (https://paperspace.com) that cater specifically to HPC, AI, etc. workloads. The main differentiators are: - much larger GPU catalog - support for new accelerators e.g. Graphcore IPUs - different pricing structure that address problematic areas for HPC such as egress

However, one of the most important differences is the lack of unrelated web services related components that pose a major distraction/headache to users that don't have a DevOps background (which AWS obviously caters to). AWS can be incredibly complicated. Simple tasks are encumbered by a whole host of unrelated options/capabilities and the learning curve is very steep. A platform that is specifically designed to serve the scientific computing audience can be much more streamlined and user-friendly for this audience.

Disclosure: I work on Paperspace.

Lambda GPU Cloud has the cheapest A100s of that group. https://lambdalabs.com/service/gpu-cloud

Lambda A100s - $1.10 / hr Paperspace A100s - $3.09 / hr Genesis A100s - no A100s but their 3090 (1/2 the speed of 100) is - $1.30 / hr for half the speed

Coreweave. I know the CTO. They are doing great work over there.

https://www.coreweave.com

https://www.genesiscloud.com/ is pretty decent

datacrunch.io has some 80G A100s