RAG Embedding Compression: Float8 Quantization Achieves 4x Storage Reduction with <0.3% Performance Loss — Combined with PCA at 50% Dimensions Yields 8x Total Compression

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Recent research on RAG embedding optimization demonstrates that low-bit floating point formats like float8 achieve 4x storage reduction with minimal (<0.3%) retrieval performance loss. Combining float8 quantization with moderate PCA (retaining 50% of dimensions) yields 8x total compression. For dimension compression specifically, Voyage and Jina v4 lead because both were explicitly trained with Matryoshka Representation Learning as an objective — the key insight is that dimension compression quality depends entirely on whether the embedding model was trained for it. Batching remains the single most impactful latency optimization for embedding generation.