How we made Cerebras-GPT with Nolan Dey and Quentin Anthony

In this episode of the Cerebras podcast we dive into the making of Cerebras-GPT. We discuss:
• The importance of open large language models
• What makes Cerebras-GPT unique among LLMs
• The tradeoffs of compute-optimal vs. inference-optimal
• The complexities of training on GPU clusters and how Cerebras simplifies it with weight-streaming
• Where the future of LLMs and AI hardware is headed
Speakers:
Nolan Dey (@DeyNolan) - Research Scientist, Cerebras
Quentin Anthony (@QuentinAnthon15) - Lead Engineer, EleutherAI
James Wang (@draecomino) - Product Marketing, Cerebras
Paper: arxiv.org/abs/2304.03208
Blog: www.cerebras.net/blog/cerebra...
Twitter: / cerebrassystems
LinkedIn: / cerebras-systems
Hugging Face: huggingface.co/cerebras

[@taurusneil]

This is an awesome conversation..the clarity of Nolan and Quentin in explaining complex AI papers is better than their 'namesakes' making movies!

[@JohnPasmore]

Fantastic video -- quite a bit of ground covered....maybe get the marketing dept to promote it...!

[@ApteraEV2024]

We are the Marketing Dept. 😉 Like Sub, Share, 😉