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The random forest language model (RFLM) has shown encouraging results in several automatic speech recognition (ASR) tasks but has been hindered by practical limitations, notably the space-complexity of RFLM estimation from large amounts of data. This paper addresses large-scale training and testing of the RFLM via an efficient disk-swapping strategy that exploits the recursive structure of a binary decision tree and the local access property of the tree-growing algorithm, redeeming the full potential of the RFLM, and opening avenues of further research, including useful comparisons with n-gram models. Benefits of this strategy are demonstrated by perplexity reduction and lattice rescoring experiments using a state-of-the-art ASR system.