INSUBCONTINENT EXCLUSIVE:
Tech giants like to boast about trillion-parameter AI designs that require enormous and expensive GPU clusters
But Fastino is taking a various approach.The Palo Alto-based startup states it has invented a new kind of AI design architecture thats
deliberately small and task-specific
The designs are so small theyre trained with low-end gaming GPUs worth less than $100,000 in total, Fastino says.The technique is bring in
Fastino has actually secured $17.5 million in seed funding led by Khosla Ventures, notoriously OpenAIs very first venture financier, Fastino
solely tells A Technology NewsRoom.This brings the start-ups total moneying to almost $25 million
It raised $7 million last November in a pre-seed round led by Microsofts VC arm M12 and Insight Partners.Our models are quicker, more
accurate, and cost a portion to train while outshining flagship models on specific tasks, says Ash Lewis, Fastinos CEO and co-founder
Fastino has actually built a suite of small designs that it sells to business consumers
Each model focuses on a particular task a business might need, like editing delicate data or summarizing corporate documents.Fastino isnt
divulging early metrics or users yet, however says its performance is wowing early users
June 5BOOK NOWIts still a bit early to tell if Fastinos method will catch on
The enterprise AI space is crowded, with business like Cohere and Databricks also promoting AI that stands out at certain tasks
And the enterprise-focused SATA model makers, including Anthropic and Mistral, likewise provide small designs
Its likewise no secret that the future of generative AI for business is most likely in smaller sized, more concentrated language models.Time
may tell, however an early vote of self-confidence from Khosla definitely doesnt harmed
In the meantime, Fastino says its focused on building an innovative AI team
Its targeting researchers at leading AI labs who arent obsessed with developing the greatest design or beating the benchmarks.Our hiring
technique is very much concentrated on scientists that possibly have a contrarian thought process to how language models are being built
