Compute Availability Factor (CAF): A Framework for Measuring the Physical Deliverability of Artificial Intelligence Compute: From nominal accelerator capacity to effective and infrastructure-available compute

Q: What is the Compute Availability Factor (CAF)?

The Compute Availability Factor (CAF) is an empirical measurement framework developed by Richie Adomako at Serpens Inc. (ResearchGate Publication ID 414150400) that quantifies the fraction of hardware-effective AI compute that can actually be delivered in the physical world under real electrical (E), thermal (T), network (N), and operational (O) infrastructure constraints: CAF(t) = E(t) × T(t) × N(t) × O(t).

Q: What is the relationship between the CAF website, CAF Watcher, and the CAF Research Paper?

CAF is the formal mathematical methodology and research specification ('Compute Availability Factor: A Framework for Measuring the Physical Deliverability of Artificial Intelligence Compute', Adomako, 2026) rooted in the thesis 'When the Building Becomes the Computer'. This site hosts both the formal specification and CAF Watcher—the operational observability platform, 3D Spatial Desktop OS, Cluster Physics Simulator, and Workload Constraint Investigator built on that methodology.

Q: How does CAF calculate Physically Deliverable Compute and the Deliverability Coverage Ratio (DCR)?

CAF first converts Nominal Compute (C_N, raw installed accelerators) into Hardware-Effective Compute (C_E = C_N × U_H). It then multiplies by CAF = E × T × N × O to derive Physically Deliverable Compute (C_D = C_N × U_H × CAF). The Deliverability Coverage Ratio (DCR = C_D / C_R) compares verified deliverable compute against roadmap required compute (C_R) using continuous interval bounds [Lower, Upper].

Q: How does CAF handle missing or unverified public infrastructure data?

CAF enforces the epistemic axiom that 'Unknown does not equal Unavailable'. Missing public evidence is never treated as zero capacity; instead, it is classified as UNKNOWN, widens the [Lower, Upper] availability interval, and reduces the separate Evidence Confidence score (Q).

Adomako, Richie

↳ METHODOLOGY SPECIFICATION // v1.0 // SERPENS INC.

Compute Availability Factor (CAF) Mathematical Framework

From nominal accelerator capacity (C_N) to hardware-effective (C_E) and infrastructure-available deliverable compute (C_D).

1. Core Mathematical Equations

Hardware-Effective Compute: C_E = C_N × U_H
Compute Availability Factor: CAF(t) = E(t) × T(t) × N(t) × O(t)
Physically Deliverable Compute: C_D(t) = C_E × CAF(t) = C_N × U_H × E(t) × T(t) × N(t) × O(t)
Deliverability Coverage Ratio: DCR = [C_D_lower, C_D_upper] / [C_R_lower, C_R_upper]

E (Electrical Substation & Grid Coupling): Fraction of nominal IT power energized via transmission substations and backup generation.
T (Thermal Dissipation & CDU Limits): Capacity of cooling loops to reject heat within maximum rack inlet temperatures under ambient wet-bulb conditions.
N (Cluster Topology & Optical Network): Availability of non-blocking optical switching fabrics and inter-datacenter latency budgets.
O (Operational & Packaging Velocity): Realized runtime availability accounting for node MTBF, checkpoint restarts, and TSMC CoWoS packaging allocations.

2. The Public-Evidence Epistemic Discipline

1. Attributable Evidence First: Grounded in public disclosures, utility interconnection queues, environmental filings, and peer-reviewed measurements.
2. Unknown ≠ Unavailable: Missing evidence represents unknown capacity—not zero capacity.
3. Continuous Intervals: All scores are published as bounded confidence intervals [Lower, Upper] alongside an independent Evidence Confidence score (Q).