Gemma 2 2B

Training compute

3.1×10²² FLOP

Parameters

2.6B

Published

Jun 24, 2024

Gemma 2 2B is an AI model developed by Google DeepMind (United States), first published in June 2024. It works in the language domain, on tasks such as language modeling/generation, chat, code generation and question answering.

Training it took an estimated 3.1×10²² FLOP of compute (estimation method: operation counting). The model has 2,600,000,000 parameters. It was trained on roughly 2T datapoints. Training ran on 512 Google TPU v5e.

Access: Open weights (restricted use). Its weights are openly available. Epoch AI rates the confidence of this record as confident.

Full record

Organization: Google DeepMind
Country of organization: United States
Domain: Language
Task: Language modeling/generation, Chat, Code generation, Question answering
Training compute: 3.1×10²² FLOP
Compute estimation method: Operation counting
Parameters: 2,600,000,000
Dataset size: 2T
Training hardware: Google TPU v5e
Chips used: 512
Training power draw: 227.4 kW
Model accessibility: Open weights (restricted use)
Open weights: Yes
Epoch confidence: Confident