ADM

Training compute

6.2×10²¹ FLOP

Parameters

559M

Published

May 11, 2021

ADM is an AI model developed by OpenAI (United States), first published in May 2021. It works in the image generation domain, on tasks such as image generation and text-to-image.

Training it took an estimated 6.2×10²¹ FLOP of compute (estimation method: hardware). The model has 559,000,000 parameters. It was trained on roughly 130.2T datapoints. Training ran on NVIDIA V100. The compute alone is estimated at $11K in 2023 dollars.

Access: Open weights (non-commercial). Its weights are openly available. The reference paper has 11,766 citations. Epoch AI rates the confidence of this record as confident.

Full record

Organization: OpenAI
Country of organization: United States
Domain: Image generation
Task: Image generation, Text-to-image
Training compute: 6.2×10²¹ FLOP
Compute estimation method: Hardware
Parameters: 559,000,000
Dataset size: 130.2T
Training hardware: NVIDIA V100
Training cost (2023 USD): $11K
Numerical format: FP16
Model accessibility: Open weights (non-commercial)
Open weights: Yes
Citations: 11,766
Epoch confidence: Confident