Scrutica
Facility-level FLOP capacity estimated against active and historical regulatory thresholds. Power-path defaults: PUE 1.15 (operator fleet disclosures: Microsoft FY25 1.16, AWS 2024 1.15, xAI Memphis 1.18, Stargate Abilene 1.12, Google fleet 1.09, Meta FY23 1.08); GPU share of IT load 0.49 (Scrutica-editorial midpoint between the in-server share of ~0.55 implied by SemiAnalysis’s 700 W / 1,275 W per-GPU server decomposition and the ~0.47 cluster-level share once external networking is added); MFU 0.40 (SemiAnalysis, Patel et al, “100,000 H100 Clusters”, 2024-06-17, reported FP16 MFU on trillion-parameter training runs, corroborated by LLaMA 3 405B at 0.384 and PaLM 540B at 0.462); dense FP16 specs (no 2:4 structured sparsity). Residuals shown alongside the EU AI Office’s ±30% measurement tolerance band from the July 2025 GPAI Guidelines. Reverse calculator solves for minimum GPUs or training days against any selected regime.
The model-counting question (“how many notable models cross threshold X?”) is Epoch AI’s territory. This page asks the facility-level question: which sites have the physical compute to put a model across the EU AI Act 1025line (or the lower China CAC line, or the rescinded US EO 14110 line), and under whose jurisdiction do they sit? Calibrated against measured cluster decompositions; aligned with the EU AI Office’s July 2025 measurement tolerance.
Facility-side: which sites have the physical compute to put a model across the EU AI Act 10²⁵ FLOP threshold (and the China CAC, US EO 14110 lines).
FACILITIES ≤ 30 DAYS (10²⁵ FLOP)
FACILITIES ≤ 90 DAYS
FACILITIES ≤ 1 YEAR
MODELS ABOVE EU
MODELS ABOVE US
EU AI Act: Systemic Risk GPAI
Cumulative training compute (total FLOP), not inference throughput. Article 51(2): "A general-purpose AI model shall be presumed to have high impact capabilities ... when the cumulative amount of compute used for its training measured in floating point operations is greater than 10^25." The July 202...
Regulation (EU) 2024/1689, Article 51(2) + Annex XIII · Official Journal of the European Union · Tier 1
Threshold-Capable Facilities by Country (within 1 year, 10²⁵ FLOP)
⚠ = facility is in a jurisdiction where EU AI Act: Systemic Risk GPAI applies
| Facility | Country | Daily FLOP | Days to 10²⁵ FLOP | Estimation Path | Regime Exposure | Expand details |
|---|---|---|---|---|---|---|
| (†) | AE | <1 | Power: 5000 MW | Not subject | ||
| AE | <1 | Power: 5000 MW | Not subject | |||
| US | <1 | Power: 2260 MW | Not subject | |||
| US | <1 | Power: 2200 MW | Not subject | |||
| US | <1 | Power: 2200 MW | Not subject | |||
| US | <1 | Power: 2000 MW | Not subject | |||
| US | <1 | Power: 1800 MW | Not subject | |||
| SA | <1 | Power: 1500 MW | Not subject | |||
| US | <1 | Power: 1400 MW | Not subject | |||
| US | <1 | Power: 1400 MW | Not subject | |||
| US | <1 | Power: 1200 MW | Not subject | |||
| US | <1 | Hardware: 700,000 GPUs (H100 assumed) | Not subject | |||
| US | <1 | Power: 1092 MW | Not subject | |||
| US | <1 | Power: 1000 MW | Not subject | |||
| US | <1 | Power: 1000 MW | Not subject | |||
| US | <1 | Power: 1000 MW | Not subject | |||
| AE | <1 | Power: 1000 MW | Not subject | |||
| US | <1 | Hardware: 550,000 GPUs (H100 assumed) | Not subject | |||
| US | <1 | Hardware: 530,000 GPUs (H100 assumed) | Not subject | |||
| US | <1 | Hardware: 500,000 GPUs (H100 assumed) | Not subject | |||
| FR | <1 | Hardware: 500,000 GPUs (H100 assumed) | SUBJECT | |||
| US | <1 | Power: 750 MW | Not subject | |||
| IN | <1 | Hardware: 450,000 GPUs (H100 assumed) | Not subject | |||
| US | <1 | Hardware: 400,000 GPUs (H100 assumed) | Not subject | |||
| US | <1 | Power: 650 MW | Not subject | |||
| US | <1 | Power: 600 MW | Not subject | |||
| US | <1 | Power: 590 MW | Not subject | |||
| US | <1 | Power: 576 MW | Not subject | |||
| US | ~1 | Power: 555 MW | Not subject | |||
| US | ~1 | Power: 531 MW | Not subject | |||
| US | ~1 | Power: 510 MW | Not subject | |||
| US | ~1 | Power: 500 MW | Not subject | |||
| SA | ~1 | Power: 500 MW | Not subject | |||
| US | ~1 | Power: 500 MW | Not subject | |||
| US | ~1 | Hardware: 300,001 GPUs (H100 assumed) | Not subject | |||
| US | ~1 | Hardware: 300,000 GPUs (H100 assumed) | Not subject | |||
| US | ~1 | Power: 467 MW | Not subject | |||
| US | ~1 | Power: 467 MW | Not subject | |||
| US | ~1 | Power: 450 MW | Not subject | |||
| US | ~1 | Power: 433 MW | Not subject | |||
| US | ~1 | Power: 425 MW | Not subject | |||
| US | ~1 | Power: 407 MW | Not subject | |||
| US | ~1 | Hardware: 230,000 GPUs (H100 assumed) | Not subject | |||
| US | ~2 | Power: 341 MW | Not subject | |||
| US | ~2 | Hardware: 202,224 GPUs (H100 assumed) | Not subject | |||
| US | ~2 | Hardware: 200,000 GPUs (H100 assumed) | Not subject | |||
| US | ~2 | Power: 300 MW | Not subject | |||
| US | ~2 | Power: 300 MW | Not subject | |||
| US | ~2 | Power: 300 MW | Not subject | |||
| US | ~2 | Power: 300 MW | Not subject |
Estimates assume continuous operation at full training capacity using dense FP16 TFLOP/s (no sparsity). Default MFU: 40%. Default interconnect efficiency: 85%. Hardware-based estimates assume H100 SXM5 where GPU model is unspecified. Power-based estimates use PUE 1.15 (operator fleet disclosures: Microsoft FY25, AWS, xAI, Stargate, Google, Meta) and 49% GPU share of IT load (Scrutica-editorial midpoint, calibrated against SemiAnalysis's 700 W / 1,275 W per-GPU server decomposition; see Calibration tab). Residuals from this calibration sit inside the EU AI Office's ±30% measurement tolerance for cumulative training FLOP under the July 2025 GPAI Guidelines. Methodology version: 1.3.2.
(†)Marked rows carry a substrate-vs-operational disambiguation; hover the marker for the per-facility caveat. The substrate row's daily-FLOP value remains the legitimate published-envelope ceiling, the caveat surfaces Phase-1 operational capacity where it materially differs.
Eleven Commission surfaces, checked on 9 June 2026, carry no published list of general-purpose AI models with systemic risk. Article 52(6) requires one. It is the Commission's duty and the only public surfacing of which models cross the line drawn by Article 51(2), the presumption of high-impact capabilities above 10²⁵ FLOP of cumulative training compute; notification, the providers' side, runs to the Commission in private by the Act's design. The table fills the gap with a reconstruction from Epoch AI's public compute estimates, every model whose estimate clears the presumption line, against the columns a register would settle and this cannot. Nothing in this table asserts that any provider missed an obligation.
| Model | Provider | Training compute (Epoch est.) | Epoch publication date | Art 111(3) cohort | Code of Practice |
|---|
Article 51(2) presumes systemic risk above 10²⁵ FLOP; the rebuttal mechanism (and downstream deployment decisions) leans on capability evaluations, and the capability evaluations the public can read are floors. The UK AI Security Institute's 13 April 2026 evaluation of Claude Mythos Preview ran a simple agent scaffold held constant for cross-model comparability. On 13 May 2026 AISI put the caveat on the page in its own words: “the cap, alongside our use of a simple agent scaffold, artificially lowers success rates and understates what models can do with more tokens and stronger scaffolds.”
Two weeks after Mythos shipped, Inspect (AISI's own evaluation framework) added deepagent() (subagent delegation, persistent memory, structured planning) in v0.3.213 (27 April 2026); the published cyber methodology has not yet adopted it. At identical FLOP cost a model can clear a deployment threshold under a single-agent eval that it would fail under a multi-agent eval. Compute keeps accumulating; capability evaluations report floors; policy cites the floors. Where the ceiling sits, no public evaluator says.
Primary sources (authority tier 1)
AISI · “Our evaluation of Claude Mythos Preview's cyber capabilities” (13 Apr 2026): aisi.gov.uk/blog/our-evaluation-of-claude-mythos-previews-cyber-capabilities
AISI · “How fast is autonomous AI cyber capability advancing?” (13 May 2026; floor-estimate admission verbatim): aisi.gov.uk/blog/how-fast-is-autonomous-ai-cyber-capability-advancing
| Public notification record |
|---|
| Grok 4 | xAI | 5 × 10²⁶Speculative | 9 Jul 2025 | Pre 2 Aug 2025 · compliance due by 2 Aug 2027 | Signatory (Safety and Security chapter only) | Not found in the enumerated sources as of 9 Jun 2026† |
| GPT-4.5 | OpenAI | 3.8 × 10²⁶Likely | 27 Feb 2025 | Pre 2 Aug 2025 · compliance due by 2 Aug 2027 | Signatory (full Code) | Not found in the enumerated sources as of 9 Jun 2026† |
| Grok 3 | xAI | 3.5 × 10²⁶Likely | 17 Feb 2025 | Pre 2 Aug 2025 · compliance due by 2 Aug 2027 | Signatory (Safety and Security chapter only) | Not found in the enumerated sources as of 9 Jun 2026† |
| GPT-5 | OpenAI | 6.6 × 10²⁵Speculative | 7 Aug 2025 | After 2 Aug 2025 · obligations apply from placement | Signatory (full Code) | Not found in the enumerated sources as of 9 Jun 2026 |
| Llama 4 Behemoth (preview) | Meta AI | 5.184 × 10²⁵Likely | 5 Apr 2025 | Unreleased per Epoch · placement trigger not publicly established‡ | Not on the signatory list | Not found in the enumerated sources as of 9 Jun 2026 |
| Gemini 1.0 Ultra | Google DeepMind | 5 × 10²⁵Speculative | 6 Dec 2023 | Pre 2 Aug 2025 · compliance due by 2 Aug 2027 | Signatory, full Code (listed as Google) | Not found in the enumerated sources as of 9 Jun 2026† |
| Llama Nemotron Ultra 253B | NVIDIA | 3.911 × 10²⁵Likely | 18 Mar 2025 | Pre 2 Aug 2025 · compliance due by 2 Aug 2027 | Not on the signatory list | Not found in the enumerated sources as of 9 Jun 2026† |
| Composer 2.5 | Cursor | 3.87 × 10²⁵Likely | 18 May 2026 | After 2 Aug 2025 · obligations apply from placement | Not on the signatory list | Not found in the enumerated sources as of 9 Jun 2026 |
| Llama 3.1-405B | Meta AI | 3.8 × 10²⁵Confident | 23 Jul 2024 | Pre 2 Aug 2025 · compliance due by 2 Aug 2027 | Not on the signatory list | Not found in the enumerated sources as of 9 Jun 2026† |
| Claude 3.7 Sonnet | Anthropic | 3.35 × 10²⁵Likely | 24 Feb 2025 | Pre 2 Aug 2025 · compliance due by 2 Aug 2027 | Signatory (full Code) | Not found in the enumerated sources as of 9 Jun 2026† |
| Grok-2 | xAI | 2.96 × 10²⁵Confident | 13 Aug 2024 | Pre 2 Aug 2025 · compliance due by 2 Aug 2027 | Signatory (Safety and Security chapter only) | Not found in the enumerated sources as of 9 Jun 2026† |
| Claude 3.5 Sonnet | Anthropic | 2.7 × 10²⁵Speculative | 20 Jun 2024 | Pre 2 Aug 2025 · compliance due by 2 Aug 2027 | Signatory (full Code) | Not found in the enumerated sources as of 9 Jun 2026† |
| Doubao-pro | ByteDance | 2.505 × 10²⁵Likely | 28 Oct 2024 | Pre 2 Aug 2025 · compliance due by 2 Aug 2027 | Not on the signatory list | Not found in the enumerated sources as of 9 Jun 2026† |
| Composer 2 | Cursor | 2.32 × 10²⁵Confident | 19 Mar 2026 | After 2 Aug 2025 · obligations apply from placement | Not on the signatory list | Not found in the enumerated sources as of 9 Jun 2026 |
| Mistral Large 2 | Mistral AI | 2.13 × 10²⁵Likely | 24 Jul 2024 | Pre 2 Aug 2025 · compliance due by 2 Aug 2027 | Signatory (full Code) | Not found in the enumerated sources as of 9 Jun 2026† |
| GPT-4 (Mar 2023) | OpenAI | 2.1 × 10²⁵Likely | 15 Mar 2023 | Pre 2 Aug 2025 · compliance due by 2 Aug 2027 | Signatory (full Code) | Not found in the enumerated sources as of 9 Jun 2026† |
| GPT-4 (Jun 2023) | OpenAI | 2.1 × 10²⁵Likely | 15 Mar 2023 | Pre 2 Aug 2025 · compliance due by 2 Aug 2027 | Signatory (full Code) | Not found in the enumerated sources as of 9 Jun 2026† |
| Nemotron-4 340B | NVIDIA | 1.8 × 10²⁵Confident | 14 Jun 2024 | Pre 2 Aug 2025 · compliance due by 2 Aug 2027 | Not on the signatory list | Not found in the enumerated sources as of 9 Jun 2026† |
| Qwen3-Max | Alibaba | 1.512 × 10²⁵Speculative | 5 Sep 2025 | After 2 Aug 2025 · obligations apply from placement | Not on the signatory list | Not found in the enumerated sources as of 9 Jun 2026 |
| Mistral Large | Mistral AI | 1.12 × 10²⁵Likely | 26 Feb 2024 | Pre 2 Aug 2025 · compliance due by 2 Aug 2027 | Signatory (full Code) | Not found in the enumerated sources as of 9 Jun 2026† |
| Pangu Ultra | Huawei | 1.069 × 10²⁵Confident | 10 Apr 2025 | Pre 2 Aug 2025 · compliance due by 2 Aug 2027 | Not on the signatory list | Not found in the enumerated sources as of 9 Jun 2026† |
| Aramco Metabrain AI | Saudi Aramco | 1.05 × 10²⁵Likely | 4 Mar 2024 | Unreleased per Epoch · placement trigger not publicly established‡ | Not on the signatory list | Not found in the enumerated sources as of 9 Jun 2026 |
| Inflection-2 | Inflection AI | 1.001 × 10²⁵Confident | 22 Nov 2023 | Pre 2 Aug 2025 · compliance due by 2 Aug 2027 | Not on the signatory list | Not found in the enumerated sources as of 9 Jun 2026† |
†For models on the market before 2 August 2025 the notification clock is genuinely unsettled. Article 111(3) defers “the obligations laid down in this Regulation” for this cohort to 2 August 2027, and it does so without carving the two-week notification duty out of that deferral; no published Commission guidance has fixed whether the duty was already live, and neither has any national court. The Commission's own FAQ states that providers of models placed on the market before 2 August 2025 “must comply with the AI Act obligations by 2 August 2027”. A blank cell here records that ambiguity, nothing about a provider.
‡Article 52(1) can run before release (“...or it becomes known that it will be met”); unreleased models above the line are retained for that reason, with placement status shown as Epoch records it.
Enforcement runs on its own clock. The Commission's GPAI enforcement powers, information requests and evaluations, with Article 101 fines behind them, switch on from 2 August 2026 under Article 113; the cohort column above tracks when obligations fall due, which is a different question.
The provider files first, and privately. Once a model meets the Article 51(1), point (a) condition, or it becomes known that it will, the provider “shall notify the Commission without delay and in any event within two weeks”; the presumption that pulls a model into that condition is Article 51(2), the 10²⁵ FLOP line. Notifications travel through the Commission's EU SEND platform (“EU SEND ensures the confidentiality, integrity, and authenticity of the information shared”) or the AI Office's dedicated mailbox, under Article 78 confidentiality. The public's side of the bargain is Article 52(6): “The Commission shall ensure that a list of general-purpose AI models with systemic risk is published and shall keep that list up to date, without prejudice to the need to observe and protect intellectual property rights and confidential business information or trade secrets in accordance with Union and national law.” Provider-side privacy is the Act's design; the register is the single public surfacing the Act provides.
That register has not appeared. Chapter V has applied since 2 August 2025 (Article 113); the publication duty carries no internal deadline, and ten months on, none of the eleven Commission surfaces enumerated in the log below carries the list or a link to it. Everything around it shipped on time. The GPAI Guidelines landed on 18 July 2025, the Code of Practice on 10 July 2025, the training-content summary template on 24 July 2025, and the serious-incident reporting template followed; the one Article 52(6) instrument, the register itself, did not. The Commission can also designate models on its own initiative (Article 51(1), point (b)); no public designation was located either. What circulates instead is third-party reconstruction from Epoch AI's compute estimates, which is what this table is, and says so.
A row appears when Epoch's point estimate of cumulative training compute clears 10²⁵ FLOP. These are estimates, not measured figures. Epoch labels each by confidence: “Confident” means within a factor of 3, “Likely” within a factor of 10, and “Speculative” within a factor of 30; the July 2025 GPAI Guidelines add a separate ±30% measurement tolerance on cumulative FLOP. Each value is copied exactly from the public database, carries Epoch's per-row vintage, and shows here at four significant figures. Crossing that line is not the same as sitting on the Union market: placement under Article 3 is a legal determination the public record does not settle per model, and two of these rows are unreleased per Epoch.
There is also a known direction to the undercount. Epoch estimates training compute, while Recital 111 directs that the cumulative total behind the Article 51(2) presumption count compute more broadly, “such as pre-training, synthetic data generation and fine-tuning”. A reconstruction built on the narrower measure marks a floor for the population the presumption reaches, not a census of it.
Nor does a row say anything about a provider's compliance. It is not a claim that the provider owed or missed a notification: Article 111(3) provides that “Providers of general-purpose AI models that have been placed on the market before 2 August 2025 shall take the necessary steps in order to comply with the obligations laid down in this Regulation by 2 August 2027”. And it is not a claim that the presumption has survived, since Article 52(2) invites a provider to rebut it and any such rebuttal is as private as the notification itself. What a published register would resolve per model, this reconstruction cannot, which is the gap it documents.
Scope of the claim: no evidence of a published list after these checks, on this date; that is not a proof of non-existence, and the check is re-run before any republication of this table. Open-web searches on 2026-06-09 for a Commission publication of the list, and for provider self-disclosures of Article 52(1) notifications, surfaced neither; only statutory-text mirrors and third-party reconstructions (Epoch-derived rosters) appear.
| Source | Result | Page vintage | Method |
|---|---|---|---|
| Commission AI regulatory framework policy page | No register, no link to one | Last update 11 May 2026 | direct fetch |
| Guidelines for providers of general-purpose AI models | No register; describes notification duty only | Last update 28 April 2026 | direct fetch |
| Commission FAQ on GPAI provider obligations | No register; states the 2 August 2027 rule for pre-2 August 2025 models | Last update 11 November 2025 | direct fetch |
| AI Act Service Desk, Article 52 (Commission-operated) | Quotes the 52(6) duty verbatim; links no list | No date displayed | direct fetch |
| Commission factpage on GPAI obligations | No register; confirms the private notification mailbox (EU-AIOFFICEGPAI-SR-PROVIDERS@ec.europa.eu) | Last update 1 August 2025 | direct fetch |
| Commission news item on the GPAI Guidelines | No register | Published 18 July 2025, updated 31 July 2025 | direct fetch |
| GPAI Code of Practice policy page | No register, no link to one | Last update 23 March 2026 | direct fetch |
| Code of Practice contents + signatory list | Signatory list present; no model register | Last update 23 April 2026 | direct fetch |
| Commission Q&A on GPAI models in the AI Act | No register; references Article 111(3) special rules | Last update 9 September 2025 | direct fetch |
| European AI Office policy page | No register; classification work described, no published list | Last update 1 June 2026 | direct fetch |
| Site-scoped searches across Commission AI Office surfaces | Zero register hits; only obligation descriptions and templates | n/a | recorded search |
Sources
Regulation (EU) 2024/1689, Articles 51, 52, 78, 111(3), 113 (tier 1) · eur-lex.europa.eu (CELEX 32024R1689)
AI Act Service Desk, Articles 51 and 52 pages (Commission-operated; Article 52(1) and 52(6) wording verified 9 June 2026) · ai-act-service-desk.ec.europa.eu/en/ai-act/article-52
European Commission, Guidelines for providers of general-purpose AI models (EU SEND platform and notification mechanics) · digital-strategy.ec.europa.eu/en/policies/guidelines-gpai-providers
European Commission, FAQ on obligations for GPAI providers (the 2 August 2027 sentence; page updated 11 November 2025) · digital-strategy.ec.europa.eu/en/faqs/guidelines-obligations-general-purpose-ai-providers
European Commission, GPAI Code of Practice contents and signatory list (page updated 23 April 2026; checked 9 June 2026) · digital-strategy.ec.europa.eu/en/policies/contents-code-gpai
Epoch AI, “Data on AI Models” (tier 2; CC-BY; database accessed 9 June 2026; per-row vintage in the export) · epoch.ai/data/ai-models
UK Government BEIS · Inspect framework CHANGELOG, v0.3.213 (27 Apr 2026; deepagent() + subagent delegation): github.com/UKGovernmentBEIS/inspect_ai/blob/main/CHANGELOG.md
European Parliament & Council · Regulation (EU) 2024/1689, Article 51(2) (10²⁵ FLOP presumption of systemic risk): eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32024R1689
Epoch's facility-side verification benchmark: estimates derived from satellite imagery (SkyFi, Sentinel-2, Google Earth), permitting documents, and corporate disclosures. Epoch's published uncertainty framing on the cited methodology page is bounded to cooling-model error: as much as 2× higher or lower than model estimates in theory (specification-data driven), with validated error of 1% in the two ground-truth cases Epoch has and ≤50% against indirect references such as chip-quantity-derived IT power. Epoch does not publish quantified compute-, cost-, or timing-confidence bounds on this methodology page. Scrutica ingests 473 of Epoch's Frontier + GPU-cluster facility rows (data_source prefix: epoch-*) into a 4,538-facility / 109-country substrate; the threshold computation against EU AI Act 10²⁵, China CAC ~10²⁴, and EO 14110 10²⁶ runs on the union.