When Is 0.1% Enough? Analyzing the Combined Effects of Dimensionality Reduction and Quantization on Text Embedding Compression
arXiv:2606.01074v1 Announce Type: new Abstract: Recent high-performing text embedding models often output high-dimensional real-valued vectors, resulting in substantial storage and computational costs. To address this issue, compression methods based on dimensionality reduction or quantization have been proposed; however, the effects of combining dimensionality reduction and quantization have not been sufficiently investigated. In this paper, we systematically examine the effectiveness of compressing text embeddings by combining dimensionality reduction and quantization, using four MTEB task f
The proliferation of increasingly complex AI models necessitates efficient methods for managing their computational and storage requirements, making compression a critical area of research right now.
Optimizing text embedding storage and computation through combined dimensionality reduction and quantization can significantly lower the cost and increase the scalability of AI applications for strategic readers.
The ability to deploy large text embedding models more efficiently will accelerate the development and adoption of AI technologies, especially in resource-constrained environments.
- · AI developers
- · Cloud providers
- · Edge AI companies
- · AI-driven SaaS platforms
- · Companies with inefficient AI infrastructure
- · High-latency data analytics providers
More cost-effective and scalable AI applications become widely available.
Increased competition in AI model deployment due to lower entry barriers related to compute and storage.
The development of more sophisticated and larger language models becomes economically viable for a broader range of enterprises.
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