Beyond the Dense Ceiling: VRAM-Resident Tiered Compression for Quantum Circuit Simulation on Consumer GPUs

State-vector simulation of n qubits requires 2^n complex amplitudes, capping a 6 GB consumer GPU at 29 qubits. We present qubit, a quantum circuit simulator whose state vector is partitioned into blocks, each stored in one of three tiers (ZERO with no storage, COMPRESSED as block-scaled int16 at 4 B/amplitude, or FULL as complex64) and resident entirely in GPU memory: promotion and demotion are kernels, and the only host traffic is scheduling metadata and measurement scalars, in contrast to prior compression frameworks that stage compressed state in host memory. Quantization error is accumulated exactly on-device; because unitaries preserve norms, per-event errors compose linearly in L2 norm, giving a conservative a-priori fidelity bound F ≥ (1−D)²/(1+D)² for total injected error D. The bound is tight at low circuit depth and becomes loose as errors accumulate, so we pair it with a random-walk estimate and direct measurement, and use its inverse to turn a user-specified fidelity target into a compression budget the runtime never exceeds. Unlike structure-aware simulators (matrix product states, stabilizer methods, or Qrack), which shrink only low-entanglement states, this compresses the genuinely dense states that have no structure to exploit: on an RTX 3060 Laptop GPU (6 GB, dense complex64 ceiling 29 qubits) qubit holds a 29-qubit fully-dense state in half its footprint at measured fidelity 0.9998, and is 3.5–6× faster than Qrack on dense circuits on the same card, while the ZERO tier still captures sparse states exactly (a 31-qubit GHZ circuit in 1 MB). An automatic planner chooses among dense, entanglement-factorized, and tiered representations from a static analysis of the circuit, so the user declares qubits, gates, and a fidelity target, and nothing else. Code and data: https://github.com/ArubikU/qubit