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REFERENCES
A Neural Scaling Law from the Dimension of the Data Manifold: https://arxiv.org/pdf/2004.10802
First 2020 OpenAI Scaling Paper: https://arxiv.org/pdf/2001.08361
GPT-3 Paper: https://arxiv.org/pdf/2005.14165
Second 202 OpenAI Scaling Paper: https://arxiv.org/pdf/2010.14701
Google Deepmind “Chinchilla Scaling” Paper: https://arxiv.org/abs/2203.15556
Nice summary of Chinchilla Scaling: https://www.lesswrong.com/posts/6Fpvc...
GPT-4 Technical Report: https://arxiv.org/pdf/2303.08774
Nice Neural Scaling Laws Summary: https://www.lesswrong.com/posts/Yt5wA...
Explaining Neural Scaling Laws: https://arxiv.org/pdf/2102.06701
High Cost of Training GPT-4: https://www.wired.com/story/openai-ce...
Nvidia V100 FLOPs: https://lambdalabs.com/blog/demystify...
Nvidia V100 Original Price: [https://www.microway.com/hpc-tech-tip... GPU model,Key Points](https://www.microway.com/hpc-tech-tip...)
Great paper on scaling up training infrastructure: https://arxiv.org/pdf/2104.04473
Eight Things to Know about LLMs: https://arxiv.org/abs/2304.00612
Emergent Properties of LLMs: https://arxiv.org/abs/2206.07682
Theoretical Motivation for Cross Entropy (Section 6.2): https://www.deeplearningbook.org/

Some papers that appear to pass the compute efficient frontier
https://arxiv.org/pdf/2206.14486
https://arxiv.org/abs/2210.11399

Leaked GPT-4 training info
https://patmcguinness.substack.com/p/...
https://www.semianalysis.com/p/gpt-4-...
https://epochai.org/blog/tracking-lar...