AI Factory Infrastructure
Purpose-built AI compute fabric for 5G RAN workloads — from model training to real-time inference at the network edge.
The AI Backbone for Next-Generation Networks
OranSense AI Factory delivers a vertically integrated AI compute stack purpose-built for 5G and 6G RAN workloads — combining NVIDIA GPU clusters, high-bandwidth networking, and a managed MLOps pipeline into a single, operationally simple platform.
From large-scale model training on multi-node GPU clusters to sub-millisecond inference at distributed edge nodes, AI Factory spans the full AI lifecycle without requiring separate infrastructure for each phase.
Native integration with the OranSense Sensing Platform and OranSence RAN stack means trained models deploy directly into live network control loops — closing the gap between AI research and production network operations.
Platform Capabilities
Every layer of the AI stack — from raw compute to production inference — managed as a unified platform.
GPU Compute Clusters
NVIDIA H100 and A100 GPU nodes orchestrated via Kubernetes with MIG partitioning for multi-tenant AI workload isolation.
Real-Time Inference Engine
NVIDIA Triton Inference Server with TensorRT optimization. Sub-2ms p99 latency for beam prediction and interference classification models.
MLOps Pipeline
End-to-end model lifecycle management — data ingestion, distributed training, versioning, A/B testing, and automated rollback.
High-Speed Fabric
400GbE InfiniBand interconnect between compute nodes. RDMA-enabled storage for low-latency training data access.
Model Observability
Real-time model performance dashboards, drift detection, and automated retraining triggers based on network KPI degradation.
Secure Enclave
Confidential computing with NVIDIA Hopper TEE support. Federated learning across operator boundaries without raw data sharing.
Use Cases
AI-Driven Radio Resource Management
Train and deploy models that dynamically allocate PRBs, adjust MCS, and manage handovers in real time — reducing interference by up to 40%.
Predictive Network Maintenance
Anomaly detection models trained on billions of counter events identify hardware degradation 72 hours before failure, enabling proactive intervention.
Intelligent Power Management
Reinforcement learning agents optimize cell sleep scheduling and transmit power, reducing RAN energy consumption by 25–35% during low-traffic periods.
Build Your AI-Native Network
Talk to our infrastructure architects about deploying AI Factory in your RAN environment.