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Yeah, I got that from the technical explanation. What's the practical use case, as in, when would I need this? [EDIT]: To maybe ask a better way: will this pr
by ur-whale 6y ago
Yeah, I got that from the technical explanation.
What's the practical use case, as in, when would I need this?
[EDIT]: To maybe ask a better way: will this practically help me train my DNN faster?
Or if I'm a cloud vendor, will this allow me to deploy cheaper GPU for my users?
I guess I'm asking about the economic value of the hack.
- lovedswain 6y agoRunning certain ML models in VMs Running CUDA in VMs Running transcoders in VMs Running <anything that needs a GPU> in VMs
- ur-whale 6y agoThis is the exact same information you posted above. Please see my edit.
- deleted 6y ago[deleted]
- Sebb767 6y ago> To maybe ask a better way: will this practically help me train my DNN faster? Probably not. It will only help you if you previously needed to train it on a CPU because you were in a VM, but this seems unlikely. It will not speed up your existing GPU in any way compared to simply using it bare-metal right now. > Or if I'm a cloud vendor, will this allow me to deploy cheaper GPU for my users? Yes. This ports a feature from the XXXX$-range of GPUs to the XXX$-range of GPUs. Since the performance of those is similar or nearly similar, you can save a lot of money this way. It will also make the entry costs to the market lower (i.e. now a hypervisor could be sub-1k$, if you go for cheap parts). On the other hand, a business selling GPU time to customer will probably not want to rely on a hack (especially since there's a good chance it's violating NVidias license), so unless you're building your on HW, your bill will probably not drop. But if you're an ML startup or a hobbyist, you can now cheap out on/actually afford this kind of setup.