These tensor cores sound exotic: "Each Tensor Core performs 64 floating point FMA mixed-precision operations per clock (FP16 multiply and FP32 accumulate) and 8 Tensor Cores in an SM perform a total of 1024 floating point operations per clock. This is a dramatic 8X increase in throughput for deep learning applications per SM compared to Pascal GP100 using standard FP32 operations, resulting in a total 12X increase in throughput for the Volta V100 GPU compared to the Pascal P100 GPU. Tensor Cores operate on FP16 input data with FP32 accumulation. The FP16 multiply results in a full precision result that is accumulated in FP32 operations with the other products in a given dot product for a 4x4x4 matrix multiply," Curious to see how the ML groups and others take to this. Certainly ML and other GPGPU usage has helped Nvidia climb in value. I wonder if Nvidia saw the writing on the wall so to speak with Google releasing their specialty hardware called the Tensor hardware that Nvidia decided to use it in their branding as well.