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    RaBitQ arrives in Vectorium with claims of up to 30x compression

    The announcement says the integration needs no training, supports 1, 2, 4 or 8 bits per component, and runs 1.4–2.2x faster than the original implementation.

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    1 Source, 15d ago, first seen 15d ago

    TLDR

    RaBitQ has been added to Vectorium, according to an announcement claiming up to 30x compression without training. The post lists support for 1, 2, 4 or 8 bits per component and claims performance 1.4–2.2x faster than the original implementation. For neural embeddings, it also claims RaBitQ beats PQ (product quantization) on every axis, with the gap growing as dimensionality increases.

    Combined views

    645

    1 Source, first seen 15d ago

    Combined views

    645

    1 Source, first seen 15d ago

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    2 comments
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    5 reposts

    Sentiment

    Positive——Negative

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    1 Source

    @SilvioMartinicoRaBitQ is now in Vectorium! Up to 30x compression, no training, 1/2/4/8 bits per component, 1.4-2.2x faster than the original implementation. Against PQ, on neural embeddings it wins on every axis, and the gap grows with dimensionality. #VectorSearch #Quantization #NeuralIR

    1 Source

    @SilvioMartinicoRaBitQ is now in Vectorium! Up to 30x compression, no training, 1/2/4/8 bits per component, 1.4-2.2x faster than the original implementation. Against PQ, on neural embeddings it wins on every axis, and the gap grows with dimensionality. #VectorSearch #Quantization #NeuralIR