AI models
InternLM
Training compute
10×10²³ FLOP
Parameters
104B
Published
Jul 6, 2023
InternLM is an AI model developed by Shanghai AI Lab and SenseTime (China and Hong Kong), first published in July 2023. It works in the language domain, on tasks such as language modeling.
Training it took an estimated 10×10²³ FLOP of compute (estimation method: operation counting). The model has 104,000,000,000 parameters. It was trained on roughly 1.6T datapoints. Training ran on NVIDIA A100 SXM4 80 GB. The compute alone is estimated at $2M in 2023 dollars.
Access: Unreleased. Its weights are not openly released. Epoch AI rates the confidence of this record as confident.
Full record
- Organization
- Shanghai AI Lab, SenseTime
- Country of organization
- China, Hong Kong
- Domain
- Language
- Task
- Language modeling
- Training compute
- 10×10²³ FLOP
- Compute estimation method
- Operation counting
- Parameters
- 104,000,000,000
- Dataset size
- 1.6T
- Training hardware
- NVIDIA A100 SXM4 80 GB
- Training cost (2023 USD)
- $2M
- Model accessibility
- Unreleased
- Open weights
- No
- 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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