Engram extends token embeddings with learned multi-token lookups to reduce computational overhead, enabling efficient DRAM/SSD offloading for large language models. The technique allows model architects to work within HBM constraints by prefetching embedding rows from slower memory tiers while computation proceeds. Benchmark results across NVIDIA and AMD GPUs show offloading embeddings to DRAM can improve performance compared to keeping them in HBM.