The conventional way of finding nearest neighbors is to do an exhaustive search of the database, where every point in the dataset is compared to every other point.

Exhaustive search does not scale well, however, and it becomes impractical with billion-scale datasets because it would be too slow.

Vector search can be used to address the billion-scale similarity search problem that many large e-commerce and social media companies are faced with.

Those companies have massive databases that are growing by the day.

The Gemini APU serves as the cornerstone of vector search solutions that address the billion-scale similarity search problem and do so with query-by-query latency in milliseconds.

This means that the Gemini APU is well-equipped for the many online applications where low latency is critical.





Search Applications


Vector Search


Molecular Search


Facial Recognition



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