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Benchmarks
Forge Pool benchmarks document the measured execution characteristics of the network under defined workload and topology conditions.
These benchmarks are intended to provide a transparent view of:
- distributed execution performance
- scaling behavior across agent networks
- orchestration overhead
- provider resource footprint
- reproducibility and deterministic replay
Forge benchmarks follow a simple principle:
Measure first. Derive second. Project cautiously.
Benchmark Classes
Forge Pool benchmarks are organized into several categories.
Provider Baseline
Measures the background resource footprint of the Forge Agent.
Examples:
- Agent idle residency
- resource usage while awaiting jobs
- background network activity
Execution Benchmarks
Measure raw distributed compute performance for specific workloads.
Examples:
- Monte Carlo ensembles
- catastrophe simulations
- distributed ETA risk models
Kernel Benchmarks
Measure the execution contract path, including:
- orchestration
- policy enforcement
- deterministic seeds
- result hashing
- replay materialization
Reliability Benchmarks
Measure system behavior under adverse conditions.
Examples:
- node loss
- degraded networks
- packet loss
- agent churn
Reading Benchmark Results
Each benchmark document separates results into three layers:
Measured
Direct observations from real runs.
Derived
Metrics computed from measured data.
Examples:
- iterations/sec
- per-agent throughput
- scaling envelope
Projected
Scaling projections under clearly stated assumptions.
These projections should not be interpreted as universal guarantees.
Interpretation Boundary
Forge Pool benchmarks measure specific workloads under defined conditions.
They should not be interpreted as universal performance claims across all workloads or environments.
Different adapters, models, and execution paths produce different performance envelopes.