Number of parameters in MCMC
I recently ran (successfully) a model with 2,958 parameters. It was on a 8 Gb Windows machine. You should be fine with 750.
I want to sample from my posterior distribution using the pymc package. 
I am wondering if there is a limit on the number of dimensions such algorithm can handle. My log likelihood is the sum of 3 Gaussians and 1 mixture of Gaussians. I have approx 750 parameters in my model. Can pymc handle such a big number of parameters?