AI Grid Intelligence Layer
A distributed AI inference fabric that spans core, edge, and far-edge nodes — delivering coordinated intelligence across the entire RAN footprint in real time.
Intelligence Distributed Across Every Node
OranSense AI Grid extends AI inference beyond centralised GPU clusters — distributing model execution across thousands of edge nodes, each contributing compute capacity to a unified, coordinated intelligence fabric.
The grid dynamically routes inference workloads to the optimal node based on latency requirements, available compute, and data locality — ensuring sub-millisecond response times for latency-critical RAN control decisions.
Built on a zero-copy data fabric with RDMA-accelerated inter-node communication, AI Grid eliminates the bottlenecks of centralised inference architectures and scales linearly as new nodes are added to the network.
Grid Topology — Live
Node utilisation % · Updated 1s ago
Platform Capabilities
Every layer of distributed AI inference — routing, execution, observability, and security — unified in a single grid fabric.
Distributed Inference Routing
Intelligent workload scheduler routes inference requests to the lowest-latency available node. Considers compute availability, thermal state, and network topology in real time.
Federated Model Execution
Large models are partitioned across multiple nodes using tensor parallelism. No single node needs to hold the full model — enabling deployment of GPT-scale models at the edge.
Zero-Copy Data Fabric
RDMA-over-Converged-Ethernet (RoCE) interconnect between grid nodes. Inference inputs and outputs move between nodes without CPU involvement, eliminating memory copy overhead.
Adaptive Load Balancing
Continuous monitoring of per-node utilisation, queue depth, and thermal headroom. Workloads are dynamically rebalanced to prevent hotspots and maintain SLA commitments.
Grid Observability
Real-time visibility into every node's inference throughput, latency percentiles, and resource utilisation. Anomalies trigger automated remediation before SLAs are breached.
Secure Multi-Tenancy
Hardware-enforced isolation between tenant workloads using NVIDIA MIG and confidential computing. Each operator's models and data remain cryptographically isolated on shared infrastructure.
Three-Tier Grid Architecture
Core, edge, and far-edge tiers work in concert — each optimised for its position in the network and its latency requirements.
Centralised GPU clusters for large-scale model training and batch inference. High-bandwidth NVLink interconnect between H100 nodes.
- NVIDIA H100 SXM5
- NVLink 4.0 interconnect
- 400GbE uplink
- Petabyte-scale NVMe storage
Regional inference nodes co-located with O-CU and near-RT RIC. Handles latency-sensitive control loop inference within 5ms.
- NVIDIA A30 / L40S
- 25GbE fronthaul
- Local NVMe cache
- O-RAN O2 interface
Ultra-compact inference accelerators embedded at O-DU and O-RU sites. Purpose-built for sub-millisecond physical layer AI.
- NVIDIA Jetson Orin
- eCPRI fronthaul
- Hardened enclosure
- Zero-touch provisioning
Deploy AI Everywhere in Your Network
Talk to our grid architects about extending AI inference to every node in your RAN infrastructure.