To run WGCNA on such a dataset, you will require a lot of RAM, assuming that you want to run it over the entire transcriptome of each cell. Moreover, I question what exactly it would mean when compared to the output of other methods such as tSNE, UMAP, psuedo-time analysis, etc.
None of us can stop you going ahead with this, but I just question what exactly it would mean. The aforementioned data reduction methods were designed specifically to reduce the computational burden of processing and interpreting scRNA-seq data. Thus, it may make more sense to run WGCNA on a certain number of principal components that account for an appreciable amount of explained variation, like > 80%.