Show HN: Can Europe train a frontier AI model on the compute it owns?

A new report and model from a developer argues that Europe can train a frontier-class AI model by federating its existing public compute, including EuroHPC supercomputers and AI Factories, achieving a model by 2028 versus 2033 for a new gigawatt datacenter. The analysis relies on grid-connection lead times and low-communication training techniques, concluding that federation is a viable stopgap while larger campuses face years-long delays.

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 . It is a short, sourced read aimed at a general audience. Title: "Do We Need OpenAI or Anthropic? Europe Has Tens of Exaflops at Home." /sammysltd/euromesh/blob/master/paper/compute-at-home.md 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 and model/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.