Metadata-Version: 2.4
Name: cooling-pue-ladder
Version: 1.0.0
Summary: The cooling ladder for AI density: rungs, PUE, freed megawatts, ride-through — CI-gateable.
Author: Margaret Nanyonga
License: MIT
Project-URL: Homepage, https://github.com/dimaggi-ai/cooling-pue-ladder
Keywords: datacenter,cooling,PUE,liquid-cooling,ai-infrastructure
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Topic :: System :: Systems Administration
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Dynamic: license-file

# cooling-pue-ladder: heat out, priced in PUE, megawatts, and seconds

**Cooling decides three things at once for an AI site: whether the rack
can exist (density), how much of the feed reaches the GPUs (PUE), and how
many seconds you survive a cooling fault (ride-through).** This repository
makes the cooling ladder executable — five rungs from legacy air to
immersion, a density gate you can put in CI, a capacity model where a PUE
point converts to megawatts, and an executable explanation — checked
against both public series — of why the industry-average PUE "plateau"
and hyperscale ~1.1 fleets [2][3] are both true at once.

**TL;DR:** run `pue-ladder validate` and five findings fall out.
**(1) Density, not efficiency, forces the ladder** — an NVL72-class
~120 kW rack [10] leaves only direct-to-chip and immersion standing; the
rear-door rung buys 2.5× density while cold-climate PUE gets slightly
*worse*. **(2) A PUE point is unqueued megawatts** — a 10 MW feed moved
from the survey-average 1.56 [2] to 1.2 frees ~1.9 MW (~1,500 H100s'
worth [11]) behind an interconnection you already own, against a median
~5-year queue for new grid capacity [8]. **(3) The plateau is two
fleets** — one adoption mechanism reproduces the 2014 (~1.7) and 2024
(~1.56) survey levels and the hyperscale ~1.1 fleets at once;
energy-weighted PUE runs ~0.32-0.35 below the survey number, so ask
buyers' questions in energy-weighted terms. **(4) Efficiency sells ride-through** — the direct-to-chip rung AI
needs today is the ladder's thermal-buffer *minimum* (~11 s at 132 kW),
making cooling-loss a first-class chaos-testing fault. **(5) Water is a
priced axis** — evaporative assist buys PUE at ~1 L/kWh-class WUE [3];
dry rejection gives the water back for a modeled +0.015-0.08 PUE
(+0.02-0.03 on direct-to-chip).

*Part of the DIMAGGI series on turning GPU capital into usable compute —
cooling sits upstream of `nominal` in `usable = nominal × network ×
scheduling × recovery × placement`: at a fixed grid feed, nominal IT
power is feed / PUE. Full analysis in [docs/study.md](docs/study.md);
all claims trace to [REFERENCES.md](REFERENCES.md).*

---

## The ladder

![The cooling ladder](figures/ladder.png)

## A PUE point is unqueued megawatts

![Capacity freed by PUE](figures/capacity.png)

## The plateau is two fleets

![Survey vs energy-weighted PUE](figures/two_fleets.png)

## Quickstart

```
pip install cooling-pue-ladder                # or: pip install -e .
pue-ladder ladder                             # the rungs, priced
pue-ladder gate --density 120 --rung contained-air   # exit 1: BLOCKED
pue-ladder capacity --feed-mw 10 --pue-from 1.56 --pue-to 1.2
pue-ladder fleet                              # the two-fleets demo
pue-ladder validate                           # model vs public record
```

`gate` exits non-zero when the rung can't cool the density — usable as a
CI gate for infrastructure-as-code. Every constant (density ceilings, PUE
floors and penalties, retrofit $/kW, thermal buffers) is an exposed field
on `cooling.ladder.RUNGS`, deliberately easy to override and rerun.

## Reproduce

```
make test        # 40 tests: invariants + the 10-point validation registry
make figures     # regenerates figures/ (needs matplotlib)
```

Python 3.10+, stdlib only; `matplotlib` only for figures. The validation
registry separates *calibrated* points (published fleet PUEs and the 2014
initial condition the constants were tuned to [2-6][13]) from *emergent*
ones (the 2024 survey level, the survey/energy-weighted divergence, and
the density verdicts [2][10][11]), and the suite fails if either kind
drifts.

## Series — turning GPU capital into usable compute

- **GPU Cluster Networking** ([network-vs-more-gpus](https://github.com/dimaggi-ai/network-vs-more-gpus)) · **GPU Cluster Scheduling** ([scheduler-vs-more-gpus](https://github.com/dimaggi-ai/scheduler-vs-more-gpus))
- **Chaos Fidelity Standard** ([ai-cluster-chaos-fidelity](https://github.com/dimaggi-ai/ai-cluster-chaos-fidelity)) · **Reliability Economics** ([reliability-economics](https://github.com/dimaggi-ai/reliability-economics)) · **Governed Autonomy** ([governed-autonomy](https://github.com/dimaggi-ai/governed-autonomy))
- **Compute↔Power Placement** ([compute-power-placement](https://github.com/dimaggi-ai/compute-power-placement)) · **Edge↔Neocloud Placement** ([edge-continuum-placement](https://github.com/dimaggi-ai/edge-continuum-placement)) · **AI-RAN ↔ Neocloud Resiliency** ([airan-neocloud-resiliency](https://github.com/dimaggi-ai/airan-neocloud-resiliency))
- **Cooling & the PUE Ladder** (this work) — heat out: the factor that converts a grid feed into nominal IT power

---

*Margaret (Maggie) Nanyonga — Founder & Principal Architect,
[DIMAGGI AI](https://dimaggi.ai).*
