AI models
OLMo-1B
Training compute
1.2×10²² FLOP
Parameters
1B
Published
Feb 1, 2024
OLMo-1B is an AI model developed by Allen Institute for AI and University of Washington (United States), first published in February 2024. It works in the language domain, on tasks such as language modeling/generation and chat.
Training it took an estimated 1.2×10²² FLOP of compute (estimation method: operation counting). The model has 1,000,000,000 parameters. It was trained on roughly 2T datapoints. Training ran on AMD Radeon Instinct MI250X,NVIDIA A100.
Access: Open weights (unrestricted). Its weights are openly available. Epoch AI rates the confidence of this record as confident.
Full record
- Organization
- Allen Institute for AI, University of Washington
- Country of organization
- United States
- Domain
- Language
- Task
- Language modeling/generation, Chat
- Training compute
- 1.2×10²² FLOP
- Compute estimation method
- Operation counting
- Parameters
- 1,000,000,000
- Dataset size
- 2T
- Training hardware
- AMD Radeon Instinct MI250X, NVIDIA A100
- Model accessibility
- Open weights (unrestricted)
- Open weights
- Yes
- Epoch confidence
- Confident
More from Allen Institute for AI,University of Washington
SourceEpoch AI, 'AI Models'. Published online at epoch.ai. Retrieved 2026-07-29 from https://epoch.ai/data/ai-models. Licensed under CC BY 4.0.
← All ai models