Wait, you're comparing M1's neural engine with Ampere's tensorcores? Apple is talking TOPS, not TFLOPS, that means M1's 11 TOPs are for inference only, not trai

Wait, you're comparing M1's neural engine with Ampere's tensorcores? Apple is talking TOPS, not TFLOPS, that means M1's 11 TOPs are for inference only, not training. By contrast, a tensorcore on Ampere does ~20 TFLOPS FP64, 78 TFLOPS FP16/BF16, and with GPU boost clock, up to 312 TFLOPs @ BF16. If you want to talk TOPS, an A100 does up to 1.2 exa-ops INT8, and 2.4 exa-tops INT4. That is, an A100 is more than 1000 times more powerful than the M1 at inferencing, while also supporting up to FP32/FP64 weights, and a 3080 is more than 500 times more powerful than the M1 neural engine.