Real-Time Inference
Nodia’s architecture is designed to deliver sub-50 ms inference latency for real-time applications — by moving AI computation from centralized data centers to edge devices across the decentralized Nodia mesh.
By processing tasks locally on Nodia Core, Edge, or Atlas nodes, we eliminate network round-trips and cloud bottlenecks, enabling new use cases in autonomy, video intelligence, and immersive technology.
Why Real-Time Matters
Self-Driving Cars
< 100 ms loop
Real-time perception for navigation
Security Cameras
< 50 ms detection
Instant alerts on object recognition
AR/VR & Gaming
< 30 ms feedback
Seamless, immersive user experiences
Inference Workflow
Model Deployment
Preloaded Models: Optimized AI models like YOLOv5, ResNet50, and BERT-small are available out of the box.
Custom Models: Upload ONNX, TensorFlow Lite, or PyTorch models via Dashboard → Models → Upload. Pin specific models to individual nodes for local caching and faster cold-start times.
Request Dispatch
Dashboard-Based Dispatch: Inference tasks are launched directly from the Nodia Dashboard with real-time visibility into node selection and execution latency.
Node Selection Logic: Nodia routes tasks to nodes based on current availability, device class (Core, Edge, Atlas), and historical uptime — ensuring optimal response time.
On-Device Execution
Optimized Inference Stack: Nodes use accelerated runtimes like TensorRT (Jetson platforms) for rapid model execution.
Smart Batching: Inferences are grouped in batches of up to 16 where possible, maximizing GPU throughput without compromising latency.
Result Delivery
Output Types: Results — such as bounding boxes, class labels, or feature vectors — are returned directly to the Dashboard or downloaded from decentralized storage.
Typical Latency: For most models, results are returned in 30–50 ms, depending on complexity and device class.
Prioritization Modes
You can choose your task’s priority level when submitting from the Dashboard:
Priority
Dispatch Speed
Fee Multiplier
Standard
Best-effort queue
Base rate
Accelerated
Prioritized dispatch
+20% budget
(Enterprise tier)
Planned for future
TBD
Real-Time Monitoring
Live Latency Charts: Track response time distribution per task directly in the Dashboard.
Throughput Stats: View inference-per-second (IPS), GPU usage, and CPU load per node.
Alerts: Set alerts on high latency, low throughput, or idle node status.
Best Practices
Model Optimization: Quantize weights (INT8/FP16), remove redundant layers, and trim input sizes to minimize inference time.
Edge Caching: Pin frequently used models to each device’s local cache to avoid startup delays.
Dynamic Batching: For variable workloads, use batch sizes of 1–4 during low demand and up to 16 during peaks for optimal performance.
Real-time inference is at the heart of Nodia’s edge-first architecture — enabling AI where it’s needed most: on-site, in real-time, and with zero reliance on cloud.
Next up: Distributed Model Training — scale your model updates across the global Nodia mesh.
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