arXiv:2610.32695v1 Announce Type: cross Abstract: Modern GPUs reach record analytical performance only when a query's working set fits within its GPU memory budget, and that budget is under growing pressure: GPUs are partitioned and shared, working sets are outgrowing high-bandwidth memory (HBM), and HBM is a fixed on-device resource whose per-dollar capacity has stagnated across generations. Lightweight compression can relieve this pressure by shrinking both footprint and bandwidth demand, but existing GPU compressed-execution techniques either bind decompression to a specific execution model or materialize decompressed columns in HBM before query execution. We present a compression subsystem for compiled GPU query engines that treats decompression as a first-class physical operator. Compression-specific logic is isolated behind a narrow backend interface, so new GPU-oriented encodings can be added without rewriting downstream operators. We implement the design in Proteus and study where decompressed values should be materialized in the GPU memory hierarchy. Our compiler-integrated shared-memory strategy fuses cooperative tile decompression with tuple-at-a-time query execution inside a single generated GPU kernel, avoiding decompressed-column materialization in HBM. Compared with HeavyDB and Sirius on GPU-resident data, fused on-chip decompression achieves competitive query times while reducing the resident memory footprint by up to 4x and memory-bandwidth consumption by up to 6x.