AlphaGo Fan

Training compute

3.8×10²⁰ FLOP

Parameters

8.2M

Published

Oct 1, 2015

AlphaGo Fan is an AI model developed by DeepMind (United Kingdom), first published in October 2015. 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 3.8×10²⁰ FLOP of compute (estimation method: hardware). The model has 8,209,984 parameters. It was trained on roughly 12.7B datapoints. The compute alone is estimated at $5K 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 likely.

Full record

Organization: DeepMind
Country of organization: United Kingdom
Domain: Games
Task: Go
Training compute: 3.8×10²⁰ FLOP
Compute estimation method: Hardware
Parameters: 8,209,984
Dataset size: 12.7B
Training cost (2023 USD): $5K
Model accessibility: Unreleased
Open weights: No
Citations: 18,175
Epoch confidence: Likely