Split Inference

Definition

Split Inference is inference architecture that divides a model between edge devices and more capable local or remote compute. In defense applications, it balances latency, bandwidth, privacy, and compute across a tactical network. The hard part is link interruption and leakage at intermediate activations, especially when systems are deployed across contested links, coalition boundaries, and mixed human-machine teams. KhanBMS treats it as a flexible mode for KhanBMS nodes that can degrade gracefully, tying the concept back to modular command, edge execution, and auditable authority.

Reference attributes

Layer

distributed inference method

Operational value

Balances latency, bandwidth, privacy, and compute across a tactical network

Primary risk

Link interruption and leakage at intermediate activations

KhanBMS role

A flexible mode for KhanBMS nodes that can degrade gracefully

Related terms

• Model Partitioning
  Dividing model layers or experts across devices so inference can run over a distributed system.
• Edge Inference
  Running AI models on tactical hardware at the point of sensing or action instead of relying on distant cloud compute.
• Mobile Ad-Hoc Network (MANET)
  Self-forming, self-healing IP network where every node is also a router.
• Fault-Tolerant Inference
  AI inference designed to keep functioning despite node loss, degraded sensors, hardware faults, or link outages.