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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/6Fpvch8RR29qLEWNH/chinchilla-s-wild-implications
GPT-4 Technical Report: https://arxiv.org/pdf/2303.08774
Nice Neural Scaling Laws Summary: https://www.lesswrong.com/posts/Yt5wAXMc7D2zLpQqx/an-140-theoretical-models-that-predict-scaling-laws
Explaining Neural Scaling Laws: https://arxiv.org/pdf/2102.06701
High Cost of Training GPT-4: https://www.wired.com/story/openai-ceo-sam-altman-the-age-of-giant-ai-models-is-already-over/
Nvidia V100 FLOPs: https://lambdalabs.com/blog/demystifying-gpt-3
Nvidia V100 Original Price: [https://www.microway.com/hpc-tech-tips/nvidia-tesla-v100-price-analysis/#:~:text=Tesla GPU model,Key Points](https://www.microway.com/hpc-tech-tips/nvidia-tesla-v100-price-analysis/#:~:text=Tesla%20GPU%20model,Key%20Points)
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/gpt-4-details-revealed
https://www.semianalysis.com/p/gpt-4-architecture-infrastructure
https://epochai.org/blog/tracking-large-scale-ai-models