arXiv:2605.28034v1 Announce Type: new Abstract: Clark Hash is a small method for storing neural embeddings in less space. It normalizes each database vector, applies a deterministic sparse signed Johnson-Lindenstrauss projection, clips the result, and stores a fixed-width scalar-quantized code. Queries stay in floating point and are scored against the stored sketches. In the default 384-dimensional sentence-embedding setting, Clark Hash stores a cosine-search vector in 48 bytes instead of 1536 bytes for dense f32 storage. This is 32x smaller. The method does not need a training pass, learned c
The continuous growth in the scale of neural embedding models and the need for efficient storage solutions are driving innovation in quantization techniques.
This development allows for significantly more efficient storage and retrieval of neural embeddings, which is critical for scaling AI applications and reducing infrastructure costs.
Neural embedding storage can become 32 times smaller, enabling broader deployment of AI applications in resource-constrained environments or at much larger scales.
- · AI application developers
- · Cloud providers
- · Hardware manufacturers (storage)
- · Companies using large-scale vector search
- · Inefficient embedding storage methods
- · Companies with high data storage costs
Reduced operational costs and increased accessibility for AI applications relying on neural embeddings.
Acceleration of new AI products and services that were previously constrained by memory or cost overheads.
Potential for new decentralized AI inference models due to much smaller model footprint.
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Read at arXiv cs.AI