Sparse Transformer (ImageNet)

Training compute

1.5×10²¹ FLOP

Parameters

152M

Published

Apr 23, 2019

Sparse Transformer (ImageNet) is an AI model developed by OpenAI (United States), first published in April 2019. It works in the image generation domain, on tasks such as image generation.

Training it took an estimated 1.5×10²¹ FLOP of compute (estimation method: hardware). The model has 152,000,000 parameters. Training ran on 64 NVIDIA V100 for about 168 hours.

Access: Unreleased. Its weights are not openly released. 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
Training compute: 1.5×10²¹ FLOP
Compute estimation method: Hardware
Parameters: 152,000,000
Training hardware: NVIDIA V100
Chips used: 64
Training time: 168 h
Training power draw: 39.5 kW
Model accessibility: Unreleased
Open weights: No
Epoch confidence: Confident