A sourced model and short report on a single question:
Can Europe stand up a sovereign frontier-class AI model now, by federating the public compute it already owns, while the gigawatt datacenters it is planning take years to connect to the grid?
The answer the model gives is yes, as a stopgap. Europe already operates tens of exaflops of public AI compute across the EuroHPC supercomputers and the national AI Factories. A 1 GW campus, by contrast, waits a mean of 7.6 years for grid power. Federated with low-communication (DiLoCo-style) training, the compute Europe already has can deliver a frontier-class model around 2028, against around 2033 for a new gigawatt campus.
The report is (built from
paper/compute-at-home.pdf
paper/compute-at-home.md
euromesh/
├── README.md
├── requirements.txt
├── paper/
│ ├── compute-at-home.md / .pdf the report
│ ├── grid_queue_dataset.md sourced 1 GW vs 40 MW grid-connection lead times
│ ├── eurohpc_substrate.md sourced EU public-compute inventory + "is it enough" math
│ ├── build_pdf.sh, _report.typ PDF build (pandoc + typst)
│ └── figures/ generated charts (PNG + SVG)
└── model/
├── MODEL_SPEC.md the model specification (equations, params, invariants)
├── RESULTS.md full results, scenarios, sensitivity, caveats
├── run.py regenerates every CSV and figure
├── src/ the three-layer model (efficiency, ramp, regions)
├── params/ hardware.yaml, training.yaml, regions.csv + SOURCES
├── results/ generated CSVs (do not hand-edit)
└── tests/ pytest suite (52 tests) + invariant self-checks
Three layers. Layer 1 is the per-FLOP efficiency of low-communication training (how much the DiLoCo penalty costs). Layer 2 is time-to-availability (when sites energize and how fast cumulative compute accrues). Layer 3 is a per-region scorecard on time, cost, carbon, and feasibility. The headline result is set almost entirely by Layer 2: it reduces to one inequality, the federation wins if its sites are online before a gigawatt campus is. The training efficiency penalty is second-order, confirmed by the sensitivity tornado.
python3 -m venv .venv
.venv/bin/pip install -r requirements.txt
.venv/bin/python -m model.run # regenerates all CSVs in model/results and figures in paper/figures
.venv/bin/python -m pytest model/tests/ # 52 passed
bash paper/build_pdf.sh # rebuilds paper/compute-at-home.pdf (needs pandoc + typst)
The run is reproducible from a clean tree: deleting every output and re-running exits 0 and regenerates everything.
Grid-connection lead times:paper/grid_queue_dataset.md
, seven regions, per-region primary sources, anchored by the AWS "up to seven years" statement and the IEA 2-to-10-year range, with limitations stated.EU public compute:paper/eurohpc_substrate.md
, the EuroHPC flagships and the 19 AI Factories, accelerator counts and the training-time math.Model parameters:model/params/SOURCES.md
andmodel/params/SOURCES_hardware_training.md
, with confidence tags.
The point of this repo is clarity, not novelty. The thesis rests on grid-queue
lead times, which are sourced central estimates rather than observed figures (no
European operator has yet energized a 1 GW point load). The compute is owned but
not yet usable for one coordinated run: the EuroHPC machines are shared,
batch-scheduled, and heterogeneous, so the addressable fraction is a political
decision rather than a hardware fact. Frontier-scale distributed training is
unproven above about 10B parameters today, so the target is a credible
frontier-class model rather than a guaranteed 405B. All of this is in
model/RESULTS.md
and the report's caveats section. Figures and dated events are as of June 2026. This is an independent model and analysis, not peer-reviewed.