In my experience FPGA>GPU for inference, if you have people who can implement good FPGA designs. And inference is more common than training. Much of this is due to explicit memory management and more memory on FPGA.
ASICs (in this case: a fully dedicated GPU) obviously wins in the situation it is designed for. The A100, and other GPU designs, probably will have higher FLOPs than any FPGA made on the 7nm node.
But not a "lot" more FLOPs, and the additional flexibility of an FPGA could really help in some problems. It really depends on what you're trying to do.
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At best, 7nm top-of-the-line GPU is ~2x more FLOPs than 7nm top-of-the-line FPGA under today's environment. In reality, it all comes down to how the software was written (and FPGAs could absolutely win in the right situation)
The original comment by brandmeyer said "ASIC", not "GPU".
Take the same RTL. Synthesize it for ASIC and for FPGA. Observe a 20x difference after normalizing for power, area, and clock speed.
In contrast: the NVidia A100 has 19 32-bit TFlops. Higher than the Xilinx chip, but the Xilinx chip is still within an order of magnitude, and has the benefits of the LUTs still.
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It should be noted that Xilinx Versal "AI engine" is a VLIW SIMD-architecture: https://www.xilinx.com/support/documentation/white_papers/wp..., effectively an ASIC-GPU hardwired into the FPGA.