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Distributed AI Inference ​

Inference is not always a single-node operation.

At scale, it becomes a distributed execution problem.

The Pattern ​

Distributed inference decomposes model execution into parallel workloads.

Execution Shape ​

text
input data
    ↓
adapter (model routing)
    ↓
distributed inference shards
    ↓
aggregation
    ↓
artifacts + replay

Primitive Composition ​

  • distributed compute execution
  • deterministic aggregation

What Gets Computed ​

  • model outputs
  • latency distribution
  • execution trace
  • capacity signals

Output Artifacts ​

text
inference_output
latency_profile
execution_trace
aggregation_metadata
capacity_signal
replay_token

Why It Matters ​

Inference becomes constrained by:

  • hardware
  • concurrency
  • latency

Why Forge ​

Forge treats inference as:

  • distributed workload
  • replayable execution
  • composable system component