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i doubt that, consumer gpus dont have a native double precision pipeline built in, so a geforce card can never be used to train the same type of modelsNope. You may, if defect rates are high enough, collect enough defective dies to throw into another SKU but if there's imperfections in the finished product that can't be worked around by fusing off a part of the core it goes in the bin.
It's possible to give up some die area and design in some redundancy so you could work around a defect, e.g make each cache 2-3% bigger so you can fuse off any defects or even fabricate your dies with extra CUDA, RT, or Tensor cores so you can fuse off defective cores. However by doing so you're giving up valuable die area, the dimensions of the dies are set at the very beginning of the fabrication process and each SKU has to use dies of the same dimension (heatsink flat plate, board size, number of solder bumps, etc, etc).
also, nvidia has pretty much outlawed the use of geforce drivers in datacenters, part of EULA.. so you cant be building out huge purpose built infrastructure with geforce cards