Efficient AI: How new models could lower token costs and RAM prices

▤ Summary

This article examines how new AI model architectures could reduce token costs and RAM requirements. It explains that the attention mechanism in transformer models drives much of the compute burden in generative AI. It also highlights alternative approaches such as xLSTM and Kimi Linear that aim to use more efficient memory structures. The piece suggests these models may first gain traction in compute-constrained environments like industry and robotics. It frames the topic as a potential shift in the economics of AI infrastructure rather than a product announcement.

▦ Linked Companies

OpenAI

$25M to $50M