>10-100us for simple models (say, a fully connected dnn with a few million parameters) I basically don't believe you. I'm a researcher in this area (DNNs on FP

>10-100us for simple models (say, a fully connected dnn with a few million parameters) I basically don't believe you. I'm a researcher in this area (DNNs on FPGAs) and you cannot get these latencies on real models without going to FPGA (and you're not synthesizing Verilog from TF, unless you're one of my competitors...). Just your kernel launch overheads for GPU are on the order of 10ms. For example, here's a talk given at GTC a couple of years ago where they do get down to 35us (on tensorcores) using persistent kernels, but on a mickey mouse network https://www.nvidia.com/en-us/on-demand/session/gtcsiliconvalley2019-s9769/ https://www.nvidia.com/en-us/on-demand/session/gtcsiliconval... CPU (where you don't have to deal with async CUDA calls) won't save you either; again here's a paper from USENIX (so you know it's legit) that shows that lowest times for real networks on CPU are ~2ms (and that's on resnet18, far shy of "millions" of weights) https://www.usenix.org/system/files/atc19-liu-yizhi.pdf https://www.usenix.org/system/files/atc19-liu-yizhi.pdf