§ {it.n}
{it.title}
{it.body}
Five named components. The descriptions below convey the shape of the work and the tools each part draws on.
A simulator that represents the cluster as a topology of typed slots and pooled spares, generates failures as time-stamped events from the per-component hazard process, and treats repair, swap, and reallocation as first-class events too. It models partial-failure semantics — a GPU can degrade in bandwidth, an HBM stack can lose a channel — and is the environment everything downstream trains and evaluates against.
), }, { n: '02', id: 'physics', title: 'Physics-of-failure-conditioned hazard rates', body: ( <>Per-component hazards are conditioned on the operating conditions that actually drive them — temperature, current, voltage and frequency cycling, mechanical wear — through standard reliability-physics scaling laws. The output is an MTBF for each slot under its real operating regime, not under datasheet conditions.
), }, { n: '03', id: 'bayesian', title: 'Bayesian hierarchical model over failure data', body: ( <>The data: LLM training papers, HPC lab disclosures, NVIDIA SDC studies, vendor RMA aggregates. A Bayesian hierarchical model is the right tool for that shape of evidence: it pools across sources, captures between-fleet variation, and produces a posterior over per-component failure rates with explicit credible intervals. The posterior tightens as design partners contribute telemetry.
), }, { n: '04', id: 'correlated', title: 'Correlated-failure modeling', body: ( <>Independent-failure assumptions are convenient and systematically wrong in the tail — bad PSU batches propagate, HBM defects cluster, environmental faults take a rack down at once. A self-exciting layer over the per-component hazards captures that clustering. The practical effect is a thicker tail on cluster-level loss, which is exactly what a parametric cover has to absorb and what a spreadsheet model under-provisions against.
), }, { n: '05', id: 'policy', title: 'Two-phase policy optimization', body: ( <>Two decision horizons, two tools. At procurement time, a greedy submodular allocation picks the spare configuration that meets the SLA at minimum cost — a one-shot decision before the cluster ships. Across the operating life, a reinforcement-learning agent — using the simulator as its environment — handles component swap, spare-pool redistribution, and repair scheduling under live telemetry. Both phases sample from the Bayesian posterior, so the resulting policies are robust to the parameter uncertainty the model honestly reports.
), }, ]; return (The pilot deployment is the permanent deployment. Both decision horizons — procurement and operations — run as the same artifact in the same place, with the same posterior and the same simulator underneath. Telemetry, contracts, and outputs do not leave the customer's environment without the customer's explicit consent.
{ph.body}
The architecture fits — operating conditions already drive the per-component hazard rate, so a control surface over cooling and power has a place in the same model.
The piece that is not yet built is the transfer function from cooling and power actions to junction temperature. We expect to close it by empirical fitting against facility telemetry, with the option to graduate to a reduced-order physical model. It is not in initial scope; we name it here so the architectural fit is honest and the scope is clear.
We publish credible intervals, not point estimates. The width of those intervals is part of the answer, not a defect to be hidden.
Our models improve as design partners contribute telemetry. We do not pretend that the available anchors alone are sufficient for every workload class.
We do not warrant SLA outcomes. We provide calibrated estimates against which buyers select their own confidence levels and against which underwriters price.