Why 1536 dimensions?

The user is asking why embedding models frequently use 1536 dimensions, specifically questioning the choice of 1536 = 3x512. They acknowledge the desirability of hardware-friendly multiples like 64, 128, 256, and 512, but are curious about the specific preference for 1536, as seen in OpenAI and other vendors. The user wants to know if 1536 is an empirically chosen balance between representation quality and memory/compute, or if there's an architectural/hardware reason for its convenience, particularly compared to 1024 or 2048.

This discussion uniquely probes the specific choice of 1536 dimensions for embedding models, unlike general inquiries about hardware-aligned dimensions, by directly comparing it to 1024 or 2048.