Dynamic deuterium (2H)‐MRSI enables mapping of metabolic fluxes in vivo, but its sensitivity is hampered by the low 2H gyromagnetic ratio and 2H‐labelled metabolite concentrations. Low‐rank denoising can enhance MRSI sensitivity by separating signal from noise. Several methods have been proposed, but the optimal approach for dynamic 2H‐MRSI remains unclear. This work compares six low‐rank denoising methods for dynamic 2H‐MRSI: four variations of spatiospectral partial separability (PS)—Global PS, Local PS, Stacked PS and SPectral and temporal INtegration for SVD (SPIN‐SVD)—as well as global–local higher order singular value decomposition (GL‐HOSVD) and tensor Marchenko–Pastur principal component analysis (tMPPCA). Performance was evaluated using realistic 2H‐MRSI brain simulations across a range of noise and B0 inhomogeneity levels, including a lesion model featuring focal metabolic alteration, and in vivo data. For simulations at the in vivo SNR and B0 level, the concentration root‐mean‐square errors (RMSE) of the fitted metabolite maps against gold standard maps were reduced by 29.3%, 24.4%, 20.2%, 33.4%, 23.5% and 21.9% following denoising with Global PS, Local PS, Stacked PS, SPIN‐SVD, GL‐HOSVD and tMPPCA, respectively. Across all simulated noise levels and all but the highest B0 inhomogeneity level, SPIN‐SVD achieved the lowest concentration RMSE and best preserved spatial distributions of 2H‐labelled water (HDO) and lactate (Lac), including lesion‐associated changes. While Global PS and Stacked PS reduced noise variance, they failed to preserve signal variations in simulated and in vivo data. SPIN‐SVD, GL‐HOSVD and tMPPCA reduced noise in vivo while maintaining spatial and temporal metabolite variations. GL‐HOSVD and tMPPCA performed similarly, with tMPPCA preferred for its computational efficiency. Both SPIN‐SVD and tMPPCA are suitable for denoising dynamic 2H‐MRSI, with SPIN‐SVD preferable for clinical applications owing to its simple implementation and superior preservation of local metabolic alterations, and tMPPCA better suited for absolute quantification of very low SNR metabolites.