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Two critical bottlenecks in developing LLMs are the scaling of computation, and the scaling of data. Focusing on these inputs is one way of effectively regulating pioneering foundational models. Data, being distributed and intangible, is hard to control. Compute, on the other hand, tangible and rooted in physical constraints, is not. Compute requires specialised hardware, such as advanced chips, produced through a highly complex and resource-intensive supply chain - a process epitomised by the cutting-edge technologies developed by companies such as ASML.

“At times like this, LPs reward track records with DPI and a lot of history,” said Laura Thompson, a partner at Sapphire Partners.

The team released a demo demonstrating Mixtral, an open-source LLM, running through their API, generating responses four times faster than other services at highly competitive rates.