Running directly on the encoded form removes the decode detour, trims I/O, and cuts memory churn. Batches flow from storage to accelerator without round-tripping large plaintext intermediates, so fewer cycles are spent moving and reshaping data and more are spent on actual model work.
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The representation is compact and uniform, which reduces bandwidth pressure and cache misses. Systems do less waiting and less warming; schedulers see steadier utilization, and cooling loads drop with the corresponding power draw. In practice, this rebalances the bill: less compute to feed the model, fewer watts to keep it cool.
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Adoption doesn’t demand a rebuild. The transparent wrapper feeds existing models and preserves outcomes consistent with plain-data runs, so you capture efficiency gains without retraining or re-architecting. When a plain copy is required, restore remains bit-perfect and verifiable.