Edge Latency

Local Edge Inference vs Cloud API Latency

For edge operations, total latency is network round-trip plus queuing plus inference. Pushing telemetry to a cloud API adds round-trip and queuing overhead that spikes under signal attenuation and packet loss. Local C++ runtimes drop both to zero, giving deterministic sub-10ms loops that survive degraded connectivity.

1. The latency model

Cloud latency is dominated by network round-trip time and queuing overhead. In high-altitude drone or remote oil-and-gas environments, round-trip time jumps from a stable baseline to multiples of it under attenuation and handoff overhead, causing cloud-dependent control loops to miss deadlines.

L_total = T_network + T_queue + T_inference

cloud:  T_network = high & variable, T_queue > 0
local:  T_network = 0, T_queue = 0  ->  L_total = T_inference

2. Why POCs fail in production

  • Network instability causes intermittent timeouts during operations
  • Queuing under load adds non-deterministic jitter to control loops
  • Connectivity loss makes the system unavailable exactly when it is needed

3. The local edge approach

  • Quantized open-weights models served by native C++ runtimes
  • On-device execution with no network dependency in the hot path
  • Deterministic, bounded inference latency suitable for real-time control

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