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D-Quant is a flexible KV cache quantization framework using driftable entropy coding to convert variable-length representations into fixed-size bitstreams, enabling efficient parallel dequantization in attention kernels and achieving up to 7x memory reduction and 3.5x throughput improvement while preserving near BF16 performance.