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 directly on .serva files without decompression. The result is automatic data preprocessing. The Chimera engine enables any existing model to operate on .serva data without retraining, preserving infrastructure investments while revamping efficiency. Internal benchmarks demonstrate 30-374x energy efficiency improvements (96-99% reduction), 4x-34x lossless storage compression, and 68x compute payload reduction without accuracy loss when compared to RNN, CNN, and MLP models on FashionMNIST and MNIST datasets. At hyperscale with one billion daily iterations, these gains translate to $4.85M savings per petabyte per training cycle. When any data flows to any model on any hardware, the AI development paradigm shifts. The bottleneck moves from infrastructure to imagination.

翻译：人工智能（AI）基础设施面临两大复合危机。计算负载——训练与推理过程中不可持续的能源与资本成本——正威胁着超越电网承载能力，并将算力集中于少数组织手中。数据混沌——项目80%的精力消耗于数据准备、转换与预处理——扼杀了开发速度，并将数据集锁定于单一模型架构。现有方法将这些问题视为独立难题，通过渐进式优化分别处理，反而增加了生态系统复杂性。本文提出ServaStack：一种通用数据格式（.serva）与一个通用AI计算引擎（Chimera）的组合。.serva格式基于激光全息原理对信息进行编码，实现无损压缩；而Chimera则将计算操作转换为表征空间，使得计算可直接在.serva文件上进行而无需解压。其结果是实现了数据的自动预处理。Chimera引擎使得任何现有模型均能在.serva数据上运行而无需重新训练，在保留基础设施投资的同时彻底革新效率。内部基准测试表明，与在FashionMNIST和MNIST数据集上运行的RNN、CNN及MLP模型相比，该系统实现了30-374倍的能效提升（降低96-99%）、4-34倍的无损存储压缩，以及68倍的计算负载减少，且无精度损失。在每日十亿次迭代的超大规模场景下，这些收益可转化为每PB数据每训练周期节省485万美元。当任何数据都能在任何硬件上流向任何模型时，AI开发范式将发生根本转变。瓶颈将从基础设施转移至想象力。