Deterministic, Reproducible Inference in Exact Integer Arithmetic, and Verifiable Distributed Inference as Its Consequence
Sayed Amir Karim · Pelloran Inc. · a preprint of November 2026, with a DOI, under CC BY 4.0
Abstract
Typical optimized floating-point inference is not reproducible across environments: the same model on different hardware, compilers, or thread counts can produce different bits, because IEEE-754 addition is not associative and platform math libraries round transcendental functions differently (reproducible floating point IS achievable, e.g. a reduction-pinned BLAS, but at a cost and not by default).
We present tritify, an open-source runtime that executes the forward pass of real models – decoder-only language models, Mixture-of-Experts decoders, and the Segment Anything 2.1 image/video stack – with the entire run-time path (forward_int) in exact integer arithmetic; floating point is confined to offline weight/eps quantization and the float reference used for verification. We make three precise, separate claims and verify each.
(1) Cross-hardware reproducibility: the integer path produces bit-identical output run-to-run, across CPU instruction-set architectures, and across CPU/GPU, carried by a reproducing SHA-256 of the output.
(2) Decision fidelity to the float reference: the integer path reproduces the float reference's decision on the models and inputs tested, with the numeric exceptions measured rather than assumed away; we extend this to depth, measuring per-layer fidelity through sixteen layers of OLMoE-1B-7B and of a 106-billion-parameter frontier MoE, and find no fidelity cliff.
(3) Location independence: because the output is a deterministic function of (weights, inputs) and is bit-identical wherever each operation runs, a model's computation can be split – across CPU instruction sets, across CPU and GPU, and across many networked machines – with zero effect on the output bits, and the gather equals the single machine exactly.
We demonstrate Claim 3 at scale: a frontier Mixture-of-Experts model, GLM-4.5-Air (106,852,251,264 parameters, 427 GB of integer codes), generated its answer across three machines none of which can hold its full integer codes in RAM – bit-identically to a single-host reference that streams the experts, carried by the same SHA-256 at every layer (a separate run split a whole model across six machines of two instruction sets and three locations with the identical hash).
The consequence is verifiable distributed inference: a fast, untrusted server (or a fleet of commodity devices) runs the model while an open scalar oracle – local, or any third party – re-derives the exact bytes and checks them against the public reference; turning honest-run agreement into a guarantee against a dishonest server is future work.
We are deliberately careful about scope: we do not claim a per-device speed improvement (distribution adds latency; the win is capacity, not speed), bit-for-bit equality of the raw floating-valued intermediates, or confidentiality – verifiability is not privacy. The contribution is exactness, determinism, portability, and, above all, verifiability: every operation ships both an integer path and a floating-point reference, and a gate refuses anything the two disagree on beyond tolerance.
This is a preprint: it reports the measurements, and the reproducibility artifact – the public repository, commit, full-length digests, and exact run commands that let a third party re-derive the exact bytes – is provided at release.