This chapter delves into the complexities of ML hardware, evaluating the evolution of architectures and memory configurations amidst the rapid growth of AI models. It also discusses the financial aspect of a company providing an inference API service compared to a competitor, focusing on cost efficiency and user throughput. The conversation scrutinizes the risks associated with specialized hardware for AI models, touching on future uncertainties and the importance of considering hardware investments in the context of evolving architectures.

Get the Snipd
podcast app

Unlock the knowledge in podcasts with the podcast player of the future.
App store bannerPlay store banner

AI-powered
podcast player

Listen to all your favourite podcasts with AI-powered features

Discover
highlights

Listen to the best highlights from the podcasts you love and dive into the full episode

Save any
moment

Hear something you like? Tap your headphones to save it with AI-generated key takeaways

Share
& Export

Send highlights to Twitter, WhatsApp or export them to Notion, Readwise & more

AI-powered
podcast player

Listen to all your favourite podcasts with AI-powered features

Discover
highlights

Listen to the best highlights from the podcasts you love and dive into the full episode