Specifically 472 GFLOPS on FP16 and 236 GFLOPS on FP32^[1]. For comparison GTX 1060 performances are 68.36 GFLOPS on FP16 and 4.375 TFLOPS on FP32^[2].

Jetson Nano overwhelmingly win on FP16, but only because GTX 1060 focused on gaming which often uses FP32^[3]. So clearly even cluster of Jetson Nano can't beat a GTX 1060

Source :
1. https://devblogs.nvidia.com/jetson-nano-ai-computing/
2. https://www.techpowerup.com/gpu-specs/geforce-gtx-1060-6-gb.c2862
3. https://www.reddit.com/r/gamedev/comments/8rzsye/why_have_most_engines_settled_on_fp32_for/