Inference runtime

HaloRT.
The same answer, every single run.

Language models are usually non-deterministic: the same input produces a different answer each inference.
You cannot evidence a control you cannot reproduce.

HaloRT is the inference technology underneath IntelCS. It makes model output reproducible: identical inputs, identical ruleset, identical result — today, and in an examination three years from now.

possible outputs run 001

A probabilistic runtime can land anywhere in the cloud. HaloRT collapses it to one exact state — the same one, on every run, from any starting point.

See it

Run the same review twice.

Same document, same requirement, repeated runs. A conventional LLM stack drifts. HaloRT does not. Watch the digest column as the runs stack up.

Input

case-88421.pdf → REQ-0148 v4

Executing run 1

Conventional LLM stack

—

run 1 ····…···· —
run 2 ····…···· —
run 3 ····…···· —
run 4 ····…···· —

Output digest changes between runs. An examiner cannot re-derive the finding, so the control cannot be evidenced by re-execution.

HaloRT

—

run 1 ····…···· —
run 2 ····…···· —
run 3 ····…···· —
run 4 ····…···· —

Identical digest, identical finding, identical cited extract. The run is evidence, and it can be reproduced on demand.

How

Determinism is engineered, not requested.

Setting a temperature to zero does not make a model reproducible. Drift comes from execution order, floating-point non-associativity, batching, hardware scheduling and silent model or configuration changes. HaloRT closes each of those.

01

Fixed execution order

Reduction and accumulation order is fixed, independent of batch composition or concurrency. Two runs perform the same operations in the same sequence.

02

Pinned numerics

Precision, kernel selection and rounding behaviour are pinned to the run, so hardware and driver changes cannot silently move an output.

03

Sealed model & config

Model weights, prompt scaffolding, tokeniser and decoding settings are sealed together and content-addressed. There is no "the model was updated" explanation available.

04

Signed run manifest

Every run emits a manifest — inputs, ruleset version, sealed model digest, environment — signed and stored with the finding. Reproduction is a command, not an investigation.

Why it matters

Four things determinism buys you that probabilistic tooling cannot.

Evidence that survives re-examination

A finding from 2024 can be re-executed in 2028 and return the same result. That is the difference between an audit artefact and an anecdote.

Rule changes with a real diff

Re-run a population under a new interpretation and every difference is attributable to the change — not to model variance. Interpretation changes become testable.

Validation and qualification are possible

Regulated quality systems require documented evidence that a computerised system performs consistently. Reproducibility makes that testable rather than aspirational.

Consistency across the population

Two identical cases receive an identical outcome — including two identical cases six months apart, in different regions, handled by different teams.

Talk with our AI risk team.

Trust is not the control. Verification is. Ask how reproduction is verified, what the manifest contains, and how sealing is enforced.

Request a technical briefing→