Mini White Paper

Deterministic, Verifiable Compute at GPU Scale

Why Bit-Exact Math, Cryptographic Hashes, and Energy Efficiency Are Becoming First-Class Infrastructure Concerns

Eric Waller | December 2025

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Executive Summary

Modern high-performance computing and AI systems are optimized for throughput, but not for reproducibility or verifiability. Floating-point nondeterminism can cause identical workloads to produce different numerical results, undermining auditability and trust. This paper introduces deterministic, verifiable compute as a practical infrastructure primitive.

Key Topics

The Hidden Problem: Floating-Point Nondeterminism

IEEE-754 defines individual floating-point operations but does not guarantee bit-exact results under parallel execution.

Why Reproducibility Is No Longer Optional

In regulated finance, autonomy, and scientific computing, nondeterministic results are no longer acceptable.

Deterministic Compute as a First-Class Primitive

A deterministic compute service enforces strict execution invariants so that identical inputs always produce bit-identical outputs.

From Reproducibility to Verifiability

By hashing output buffers with SHA-256, computation becomes externally verifiable without sharing internal implementation details.

Performance Without Compromise

Independent third-party validation demonstrates that deterministic execution can coexist with high GPU throughput and low latency.

Power, Heat, and Cooling

Energy efficiency matters at the facility level. Reductions in IT power consumption reduce total facility power via PUE.

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