Pilot concept · EdgeOps · Offline dual-LLM · NVIDIA or AMD
A KingAI pilot concept for higher-trust local AI: two LLMs running on two separate NVIDIA or AMD edge devices. One analyses, the other independently reviews. Agreement raises confidence, disagreement creates a clear review flag, and the full evidence trail is captured locally.
A single AI model at the edge has a single point of failure and no second opinion. This concept runs two independent local models on two physical systems, so their outputs can be cross-checked before they become a report, alert or evidence pack.
System A ingests the raw data and produces findings. System B, running a different model on separate hardware, independently reviews the same inputs. Agreement raises confidence; disagreement is surfaced as a decision flag, not buried. Every input, output, confidence score and adjudication result is logged.
Two edge devices, two independent models, one adjudication record. Agreement, disagreement, confidence and supporting evidence are captured before the output leaves the local workflow.
Two independent models must agree before an output is treated as high confidence. Disagreement is a signal, reducing the risk of a single model's hallucination going unchecked.
Two physical systems mean no single point of failure. If one device is lost to power, damage or a denied environment, the other continues in a degraded-but-operational mode.
The concept is designed to run on NVIDIA or AMD accelerators, so capability is not locked to a single vendor or supply chain, which supports sovereign resilience.
Both models run locally with no cloud dependency. Sensitive data stays on the devices, suitable for air-gapped, disconnected and controlled environments.
Agreements pass forward at high confidence; disagreements and low-confidence findings are captured as review flags with the competing reasoning preserved.
Both models' outputs, the cross-check result and the adjudication are logged with sources and timestamps for review, replay and after-action reporting.
Near-term, commercial off-the-shelf hardware. The same workflow is designed to run on either vendor, chosen to suit the deployment, budget and supply chain.
Jetson Orin class modules for rugged, low-power edge nodes; RTX workstation or laptop GPUs where heavier local models are needed. CUDA-based local inference, quantised models.
Ryzen AI mini-PCs and Radeon GPUs for local inference, plus AMD-based mini-servers. ROCm / DirectML-style local inference, quantised models.
The two systems can run different vendors and different model families on purpose, so the review model is genuinely independent of the analysis model.
Success is measured, not assumed: agreement rate between the two models, how much load reaches a human, time saved versus manual review, and the catch rate on deliberately injected errors.
KingAI can shape this around operational reporting, fleet sustainment, telemetry assessment, exercise capture, firmware review, field notes, local document intelligence or partner-specific EdgeOps workflows. See the broader pilot pathways and EdgeOps for how it fits.
The offline edge-AI reporting and decision-support layer this concept plugs into.
Open EdgeOpsLocal model setup, private workflows and Australian-owned capability development.
Open Local AIThe longer hardware pathway from COTS accelerators toward custom edge devices.
Open roadmapHow dual-use, resilient, offline AI fits AUKUS Pillar II and Defence-adjacent pathways.
Open DefenceBring one reporting problem, evidence source, log set, transcript set, telemetry stream or document workflow. KingAI can scope a two-device, dual-LLM pilot with cross-checking, disagreement detection, confidence scoring and full evidence capture.