“Revenue by 2027.5” needs to mean “revenue between summer 2026 and summer 2027″. And the time when the $150bn is raised needs to be late 2026, not “2027.5”, in order to actually build the thing by early 2027 and have it completed for several months already by mid to late 2027 to get that 5e28 BF16 FLOPs model. Also Nvidia would need to have been expecting this or similar sentiment elsewhere months to years in advance, as everyone in the supply chain can be skeptical that this kind of money actually materializes by 2027, and so that they need to build additional factories in 2025-2026 to meet the hypothetical demand of 2027.
By “used for inference,” this just means basically letting people use the model?
It means using the compute to let people use various models, not necessarily this one, while the model itself might end up getting inferenced elsewhere. Numerous training experiments can also occupy a lot of GPU-time, but they will be smaller than the largest training run, and so the rest of the training system can be left to do other things. In principle some AI companies might offer cloud provider services and sell the time piecemeal on the older training systems that are no longer suited for training frontier models, but very likely they have a use for all that compute themselves.
“Revenue by 2027.5” needs to mean “revenue between summer 2026 and summer 2027″. And the time when the $150bn is raised needs to be late 2026, not “2027.5”, in order to actually build the thing by early 2027 and have it completed for several months already by mid to late 2027 to get that 5e28 BF16 FLOPs model. Also Nvidia would need to have been expecting this or similar sentiment elsewhere months to years in advance, as everyone in the supply chain can be skeptical that this kind of money actually materializes by 2027, and so that they need to build additional factories in 2025-2026 to meet the hypothetical demand of 2027.
It means using the compute to let people use various models, not necessarily this one, while the model itself might end up getting inferenced elsewhere. Numerous training experiments can also occupy a lot of GPU-time, but they will be smaller than the largest training run, and so the rest of the training system can be left to do other things. In principle some AI companies might offer cloud provider services and sell the time piecemeal on the older training systems that are no longer suited for training frontier models, but very likely they have a use for all that compute themselves.