OranSense
AI inference GPU edge computing infrastructure
Platform

Inference Layer

Ultra-low latency AI inference at the 5G edge — purpose-built for real-time RAN intelligence, xApp execution, and autonomous network decision-making.

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<2msInference latency
500+Models deployed
99.99%Inference uptime
10xThroughput vs CPU

What It Does

AI Inference at Network Speed

The OranSense Inference Layer sits directly in the 5G data path, executing AI models with sub-2ms latency — fast enough to influence RAN scheduling, beam management, and slice control in real time.

Built on NVIDIA Triton Inference Server and optimised for edge GPU hardware, it supports TensorRT, ONNX, and PyTorch models with dynamic batching, model versioning, and zero-downtime hot-swap.

Telemetry Stream100%
Feature Pipeline85%
GPU Inference72%
Action Dispatch68%
End-to-end: 1.8ms avg
NVIDIA Triton

Capabilities

Built for the Edge

Sub-2ms Latency

Hardware-accelerated inference on edge GPUs with TensorRT optimisation — delivering decisions before the next scheduling interval.

Multi-Model Serving

Run hundreds of concurrent models across RAN, core, and edge functions with intelligent resource partitioning and priority queuing.

Hot-Swap Deployment

Update, roll back, or A/B test models in production with zero downtime — version-controlled and auditable.

Adaptive Batching

Dynamic micro-batching maximises GPU utilisation without sacrificing latency SLAs — automatically tuned per model profile.

Inference Guardrails

Confidence thresholds, drift detection, and fallback policies ensure safe autonomous decisions even under distribution shift.

Federated Inference

Distribute inference across a hierarchy of edge nodes with automatic routing to the nearest capable accelerator.

Architecture

Inference Pipeline

01Telemetry IngestionReal-time KPI streams from RAN elements ingested via Kafka at sub-100ms intervals.
02Feature ExtractionStreaming feature pipelines transform raw telemetry into model-ready tensors with online normalisation.
03Model ExecutionTensorRT-optimised models execute on edge GPUs with dynamic batching and priority scheduling.
04Action DispatchInference outputs are translated into xApp control messages and dispatched to the Near-RT RIC via E2 interface.

Model Registry

Deployed Models

ModelTypeLatencyAccuracy
BeamNet-v3
Beam Management1.2ms
97.4%
LoadBalancer-AI
Traffic Steering0.9ms
98.1%
AnomalyGuard
Fault Detection1.8ms
99.2%
SliceOptimizer
Slice Management1.4ms
96.8%

Deploy AI at Network Speed

Bring sub-2ms inference to your 5G edge infrastructure with the OranSense Inference Layer.