实时
人工智能进展

人工智能进展的现状

算力、模型、芯片、数据中心与基准测试:人工智能的完整发展轨迹,量化呈现并持续更新。

训练算力随时间的变化

1950 至 2026 年间每一个已知训练算力的追踪模型——对数刻度上跨越 25 个数量级。

绘制的模型
1,399
前沿翻倍周期
5.4 个月
前沿
其他模型
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
数据集
跨维度视图
这些数字如何得出

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.

阅读完整方法论
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.
最近同步 2026-07-29