The last level TLB (LLT) and the last level cache (LLC) play a critical role in the overall performance of memory-intensive applications. While management of LLC content has received significant attention, the same may not be true for LLT. In this work, we first explore the well-known concept of dead blocks in caches for TLBs. We find thatdeadpagesare fairly common in the LLT. Different from dead blocks in LLCs, dead pages in LLTs are most often dead-on-arrival, i.e., they produce zero hits in the TLB. We design a storage-efficient dead page predictor that works with a fraction of storage compared to typical dead block predictors. This is important since an LLT itself requires only a few KBs of storage compared to MBs in LLC. We then leverage the dead page information to guide a simple dead block predictor in LLC. This is driven by the observation that dead blocks are often concentrated within dead pages. In effect, we designed a dead page predictor and a correlating dead block predictor with a total storage overhead of only 11KB to bypass predicted dead pages and dead blocks in LLTs and LLCs, respectively. Together, these predictors help improve the IPC of a set of 14 memory-intensive workloads by 8.3%, on average.