Edge AI Inference at 5G Scale: Architecture Decisions That Matter

The conventional wisdom in AI deployment is to train large models in the cloud and serve them from centralized infrastructure. This works well when latency is measured in hundreds of milliseconds and data volumes are manageable. It does not work at the 5G edge.

A 5G base station generates continuous streams of channel measurements, scheduling decisions, and radio resource management events. The data rates are high — a busy urban cell can produce gigabytes of telemetry per hour. The latency requirements are tight — meaningful RAN optimization requires decisions in the 10–100 millisecond range. And the hardware is constrained — edge nodes are not data center servers.

These constraints force architectural choices that are fundamentally different from cloud AI deployment. Getting them right is the difference between a system that works in a lab and one that works in a production network.

OranSense's Inference Layer is a purpose-built runtime for AI inference at the 5G edge. It is not a general-purpose inference server with edge-specific configuration. It is designed from the ground up for the specific requirements of RAN intelligence workloads.

The first constraint is compute. Edge nodes run NVIDIA Jetson or A100/H100 GPUs in compact form factors, with thermal envelopes and power budgets that are a fraction of data center hardware. Running full-precision transformer models on this hardware is not viable.

Every model deployed through the Inference Layer is quantized to INT8 or FP8 precision and compiled with TensorRT for the specific GPU architecture of the target node. This is not a one-size-fits-all optimization — the compilation is hardware-specific, and the quantization calibration is dataset-specific. The result is models that run 4–8x faster than their full-precision equivalents with less than 1% accuracy degradation on our benchmark datasets.

The second constraint is throughput. A single edge node may be serving inference requests from dozens of cells simultaneously. Naive request-by-request inference would leave GPU utilization at 10–20%. Effective batching is essential.

Our inference scheduler uses a dynamic batching algorithm that groups requests by model type and urgency, fills batches to the GPU's optimal occupancy point, and prioritizes latency-sensitive requests (real-time RAN control) over throughput-sensitive ones (analytics and reporting). The scheduler runs on the CPU while the GPU executes the current batch, eliminating scheduling overhead from the critical path.

The third constraint is operational continuity. Models need to be updated — to incorporate new training data, to fix bugs, to adapt to new RAN configurations. In a production network, model updates cannot cause service interruptions.

The Inference Layer implements hot-swap model updates: a new model version is loaded into a shadow slot, validated against live traffic in shadow mode (inference runs but outputs are not acted upon), and then promoted to active with zero-downtime cutover. If the new model underperforms, rollback is instantaneous.

Inference is only as good as the data that feeds it. At the 5G edge, data quality is a significant challenge.

RAN telemetry is noisy. Counters reset unexpectedly. Measurements arrive out of order. Vendor-specific encodings require normalization. Missing values are common during cell restarts and handovers. A naive inference pipeline that feeds raw telemetry directly to models will produce unreliable results.

Our data pipeline implements a multi-stage processing chain:

Ingestion — Telemetry streams are ingested via vendor-specific adapters that normalize data to a common schema. We support NETCONF/YANG, O1 interface, and proprietary vendor APIs.

Validation — Each measurement is validated against expected ranges and consistency rules. Anomalous values are flagged and either corrected (if the correction is unambiguous) or excluded from the inference window.

Feature Engineering — Raw measurements are transformed into the feature representations that our models expect. This includes temporal aggregation (rolling windows of 1s, 10s, 60s), spatial aggregation (cell-level, sector-level, site-level), and derived features (load ratios, interference indicators, mobility metrics).

Serving — Processed features are served to the inference engine via a low-latency in-memory store. The serving layer maintains the most recent feature vector for each cell, updated continuously as new measurements arrive.

Every component in the inference pipeline has a latency budget, and those budgets are enforced. The total end-to-end latency from measurement arrival to inference output must fit within the Near-RT RIC's 10ms–1s operating window.

Our typical latency breakdown for a real-time RAN optimization inference:

• Telemetry ingestion and normalization: 2–5ms
• Feature engineering: 1–3ms
• Inference (quantized INT8 model on GPU): 1–4ms
• Output routing to RIC: 1–2ms

Total: 5–14ms, comfortably within the Near-RT RIC window for most workloads.

For the most latency-sensitive applications — beam management, scheduling hints — we maintain pre-computed feature vectors and use streaming inference that updates outputs as new measurements arrive, rather than waiting for a complete feature window.

One of the hardest problems in production AI systems is understanding why a model made a specific decision. This is especially important in RAN optimization, where a bad decision can degrade service for thousands of users.

The Inference Layer records every inference decision with its full input feature vector, model version, and output. This creates a complete audit trail that allows engineers to replay any historical decision, test alternative models against the same inputs, and identify the specific features that drove a particular output.

We also run continuous model performance monitoring, comparing inference outputs against ground truth labels (derived from subsequent network measurements) to detect model drift. When a model's accuracy degrades below a threshold, an alert fires and the model is flagged for retraining.

Building production AI inference at 5G scale taught us several things that are not obvious from the literature:

Hardware heterogeneity is the norm, not the exception. Real networks have a mix of GPU generations, memory configurations, and thermal constraints. A deployment system that assumes homogeneous hardware will fail in production.

Data quality dominates model quality. We spent more engineering time on the data pipeline than on the models themselves. A mediocre model on clean data outperforms a sophisticated model on noisy data.

Operational tooling is not optional. The ability to update models, roll back changes, and debug decisions in production is not a nice-to-have. It is what separates a research prototype from a production system.

Latency tails matter more than averages. A system with a 5ms average latency and a 50ms 99th percentile will miss its SLA in production. We optimize for the tail, not the mean.

These lessons shaped every architectural decision in the Inference Layer. The result is a system that has been running in production networks for two years without a latency SLA violation.