In this paper we present techniques for efficient speaker recognition of a large population of speakers and for efficient speaker retrieval in large audio archives. We deal with aspects of both time and storage. We use Gaussian mixture modeling (GMM) for representing both train and test sessions and show how to perform speaker recognition and retrieval efficiently with only a small degradation in accuracy compared to classic GMM based recognition. We present techniques for achieving a dramatic acceleration of both tasks. Finally, we present a GMM compression algorithm that decreases considerably the storage needed for speaker retrieval.