The .serva Standard: One Primitive for All AI Cost Reduced, Barriers Removed
Rachel St. Clair, John Austin Cook, Peter Sutor Jr., Victor Cavero, Garrett Mindt

TL;DR
The paper introduces ServaStack, a universal data format and compute engine that drastically reduces AI training and inference costs by enabling lossless data compression and direct computation on compressed data, improving efficiency and flexibility.
Contribution
It presents the .serva data format and Chimera compute engine, enabling lossless compression and direct computation on compressed data, reducing costs and simplifying AI infrastructure.
Findings
30-374x energy efficiency improvements
4x-34x lossless storage compression
68x compute payload reduction
Abstract
Artificial Intelligence (AI) infrastructure faces two compounding crises. Compute payload - the unsustainable energy and capital costs of training and inference - threatens to outpace grid capacity and concentrate capability among a handful of organizations. Data chaos - the 80% of project effort consumed by preparation, conversion, and preprocessing - strangles development velocity and locks datasets to single model architectures. Current approaches treat these as separate problems, managing each with incremental optimization while increasing ecosystem complexity. This paper presents ServaStack: a universal data format (.serva) paired with a universal AI compute engine (Chimera). The .serva format achieves lossless compression by encoding information using laser holography principles, while Chimera converts compute operations into a representational space where computation occurs…
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Taxonomy
TopicsNeural Networks and Reservoir Computing · Distributed and Parallel Computing Systems · Parallel Computing and Optimization Techniques
