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Comment by oblivuslimited | Hacker News Reader

We use the same graphic buses, such as SXM4 and PCI Express 4.0 x 16, as you would find in your system. Theoretically, there should be no difference in performance or compatibility at all.

If you have conducted your tests on shared instances and have observed such differences, then it's understandable. However, I want to emphasize once again that we provide dedicated virtual machines, which means that the resources are exclusively allocated to each user and not shared.

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> Theoretically, there should be no difference in performance or compatibility at all.

At least in the past there was a tendency by NVIDIA to run their professional GPUs at a lower frequency, offering reliability and correctness guarantees as tradeoff. Meanwhile most of the retail/gaming GPUs came overclocked and almost outright guaranteeing that they would start to crash and fail if you tried to run them 24/7.

Even so, an A100 is not actually faster than an RTX 4090, right? So basically the reason you would want to use something like this instead is because you need the VRAM?

Yes, 0x008 makes a valid point regarding this topic. Data center GPUs and RTX Series use different technologies and optimizations for different purposes.

Our intention is not to discourage anyone from purchasing their own GPUs. We offer our GPU cloud services as an alternative for those who may not have the resources or prefer the convenience and flexibility of renting GPUs on-demand. We apologize if the discussion took a different direction, and we appreciate your understanding.

That’s not the case. It is significantly faster, at least for deep learning workloads. Check out Lambda Lab’s benchmarks.

https://lambdalabs.com/gpu-benchmarks for those who are curious, ends up being quite the difference no matter which way you slice it, up to 60% better. Of course, this is probably due to the increased interconnect bandwidth and memory rather than raw compute horsepower, but for the workloads in question that's relevant.

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