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Looks great!! Looking forward to Mac OS support!
by noway421 5y ago
Looks great!! Looking forward to Mac OS support!
- zeusk 5y agosarcasm? This requires RTX cards and afaik Apple hasn't supported Nvidia hardware since like maxwell?
- asteroidbelt 5y agoI suspect they don't really need GPU to render it. It is usually training what requires a lot of GPU, not evaluation. So the Nvidia requirement is only to sell more cards.
- etaioinshrdlu 5y agoNot true. Big neural nets like these are still dog-slow on CPUs.
- asteroidbelt 5y agoMaybe they are. But I suspect 10 core i9 CPU is not much slower than the oldest Nvidia card they list as the requirement. Don't know much about GPU performance though except random links I have found online which tell that GPU is 3-5 time faster for ML.
- faeyanpiraat 5y agoThey list nvidia RTX as their minimum requirement. i9-7980XE: 1.3 teraflops RTX 2060: 52 teraflops
- deleted 5y ago[deleted]
- asteroidbelt 5y agoI think the flops comparison you’ve presented is not fair: for nvidia it is “tensor” floops, not generic float multiplication (which is 10 times smaller), while for intel it is any float multiplication. So for i9 the number would be higher if fma operations used, no?
- programmer_dude 5y agoTensor flops is significant since this is exactly the use case for which it was designed. So IMO the comparison is fair.
- deleted 5y ago[deleted]
- asteroidbelt 5y agoIt doesn’t make sense. Why it is fair to compare matrix multiplication with generic float operations? It should be either comparison of matrix multiplication to matrix multiplication or generic float to generic float.
- etaioinshrdlu 5y agoWell, one confounding factor is that CPU Flops are more generic, for any algorithm. GPU Flops as mentioned work better on tensor cases. However, when we do have tensors, the GPU and CPU would both work to their full potential, and thus the flops comparison ought to be valid.
- ianhorn 5y agoIt wouldn't be a smooth app, but it would still render, which would be fun to play with.