AlphaGo Lee

Training compute

1.9×10²¹ FLOP

Published

Jan 27, 2016

AlphaGo Lee is an AI model developed by DeepMind (United Kingdom), first published in January 2016. It works in the games domain, on tasks such as go. It counts among the frontier models: the systems trained with the most compute of their moment.

Training it took an estimated 1.9×10²¹ FLOP of compute (estimation method: comparison with other models). It was trained on roughly 300M datapoints. The compute alone is estimated at $22K in 2023 dollars.

Access: Unreleased. Its weights are not openly released. The reference paper has 18,175 citations. Epoch AI rates the confidence of this record as speculative.

Full record

Organization: DeepMind
Country of organization: United Kingdom
Domain: Games
Task: Go
Training compute: 1.9×10²¹ FLOP
Compute estimation method: Comparison with other models
Dataset size: 300M
Training time: 696 h
Training cost (2023 USD): $22K
Model accessibility: Unreleased
Open weights: No
Citations: 18,175
Epoch confidence: Speculative