Evaluating tissue microstructure and membrane integrity in the living human brain through diffusion water exchange imaging is challenging due to requirements for a high signal-to-noise ratio and short diffusion times dictated by relatively fast exchange processes. The goal of this work was to demonstrate the feasibility of in vivo imaging of tissue micro-geometries and water exchange within the brain gray matter using the state-of-the-art Connectome 2.0 scanner equipped with an ultra-high-performance gradient system (maximum gradient strength = 500 mT/m, maximum slew rate = 600 T/m/s). We performed diffusion MRI measurements in 15 healthy volunteers at multiple diffusion times (13–30 ms) and b -values up to 17.5 ms/μm 2 . The anisotropic Kärger model was applied to estimate the apparent exchange time between intra-neurite and extracellular water in gray matter. The estimated exchange time across the cortical ribbon was around (median ± interquartile range) 13 ± 8 ms on Connectome 2.0, substantially faster than that measured using an imaging protocol compatible with Connectome 1.0-alike systems on the same cohort. Our investigation suggested that the apparent exchange time estimation using a Connectome 1.0 compatible protocol was more prone to residual noise floor biases due to the small time-dependent signal contrasts across diffusion times when the exchange is fast (≤20 ms). Furthermore, spatial variation of exchange time was observed across the cortex, where the motor cortex, somatosensory cortex, and visual cortex exhibit longer apparent exchange times than other cortical regions. Non-linear fitting for the anisotropic Kärger model was accelerated 100 times using a GPU-based pipeline compared with the conventional CPU-based approach. This study highlighted the importance of the chosen diffusion times and measures to address Rician noise in diffusion MRI (dMRI) data, which can have a substantial impact on the estimated apparent exchange time and require extra attention when comparing the results between various hardware setups.