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AI の進展

人工知能の進展の現在地

計算資源、モデル、チップ、データセンター、ベンチマーク。AI の歩みを数値で捉え、継続的に更新します。

学習計算量の推移

学習計算量が判明している追跡対象モデルのすべて(1950〜2026 年)。対数目盛で 25 桁にわたります。

描画したモデル
1,399
フロンティアの倍化
5.4 か月
フロンティア
その他のモデル
Largest training run
5×10²⁶ FLOP
Grok 4 · xAI
Tracked data-center power
11.86 GW
across 75 sites
Tracked data-center compute
12.5M H100e
H100 equivalents
Top capability index
161.7
GPT-5.6 Sol (max) · ECI
データセット
横断ビュー
これらの数値の求め方

Almost nothing here is disclosed by the labs. Training compute is reconstructed from hardware counts and training time; data-center capacity is inferred from construction permits, satellite imagery and power contracts; chip supply is modelled from earnings and shipment data. So every estimate carries its uncertainty with it — a confidence tag on an attribution, a 5th-to-95th percentile band on a supply figure, a note on how a compute number was derived. Those qualifiers are kept on the page rather than rounded away.

The underlying research is Epoch AI's, published under a Creative Commons Attribution license. What is added here is structure: one durable URL per model, per data center, per cluster, per chip, per benchmark — server-rendered, cross-linked and citable, instead of locked inside an interactive explorer.

方法論の全文を読む
SourceEpoch AI, 'AI Models'. Published online at epoch.ai. Retrieved 2026-07-29 from https://epoch.ai/data/ai-models. Licensed under CC BY 4.0.
最終同期 2026-07-29