ArcFace embeddings quantized to 16-bit pgvector HALFVEC ? [D]

This Reddit discussion explores the practical trade-offs of storing ArcFace face recognition embeddings in pgvector's HALFVEC type, which uses 16-bit floating point numbers to represent vector components instead of the standard 32-bit floats . The thread likely examines whether the 50% reduction in vector memory and storage costs comes without significant loss in search quality or recall when applied specifically to ArcFace's highly discriminative facial feature embeddings. Key concerns probably include whether the subtle precision differences lost during quantization affect nearest-neighbor retrieval, given that half-precision retains only 10 fraction bits versus 23 in full float32 , which may be more consequential for biometric similarity tasks than for general text or semantic embeddings.

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Source: r/MachineLearning | 2026-04-12