We explore the potential of optically pumped magnetometers (OPMs) to non-invasively infer the laminar origins of neural activity. OPM sensors can be positioned closer to the scalp than conventional cryogenic magnetoencephalography (MEG) sensors, opening an avenue to higher spatial resolution when combined with high-precision source space modelling. By simulating the forward model projection of single dipole sources at deep and superficial cortical surfaces onto OPM sensor arrays with varying sensor densities and measurement axes, and employing sparse source reconstruction approaches, we find that laminar inference with OPM arrays is possible at relatively low sensor counts under moderate-to-high signal-to-noise ratios (SNR). We observe improvements in laminar inference with increasing spatial sampling densities and measurement axes and demonstrate the advantage of placing the sensors closer to the scalp for inferring the laminar origins of cortical sources. However, challenges remain, such as biases towards both the superficial and deep surfaces at very low SNRs and a notable bias towards the deep surface when combining empirical Bayesian beamformer (EBB) source reconstruction with a whole-brain analysis. Adequate SNR through appropriate trial numbers and shielding, as well as precise co-registration, is crucial for reliable laminar inference with OPMs.