AI models
MAP-Neo
Training compute
1.9×10²³ FLOP
Parameters
7B
Published
Jul 10, 2024
MAP-Neo is an AI model developed by University of Waterloo, 01.AI and Wuhan University (Canada and China), first published in July 2024. It works in the language domain, on tasks such as language modeling/generation, question answering, quantitative reasoning and 2 more.
Training it took an estimated 1.9×10²³ FLOP of compute (estimation method: operation counting). The model has 7,000,000,000 parameters. It was trained on roughly 4.5T datapoints. Training ran on 512 NVIDIA H800 SXM5.
Access: Open weights (unrestricted). Its weights are openly available. Epoch AI rates the confidence of this record as confident.
Full record
- Organization
- University of Waterloo, 01.AI, Wuhan University
- Country of organization
- Canada, China
- Domain
- Language
- Task
- Language modeling/generation, Question answering, Quantitative reasoning, Translation, Code generation
- Training compute
- 1.9×10²³ FLOP
- Compute estimation method
- Operation counting
- Parameters
- 7,000,000,000
- Dataset size
- 4.5T
- Training hardware
- NVIDIA H800 SXM5
- Chips used
- 512
- Training power draw
- 707.2 kW
- Model accessibility
- Open weights (unrestricted)
- Open weights
- Yes
- Epoch confidence
- Confident
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.
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