How Small Can Google's New EmbeddingGemma 2 Get?

Google DeepMind’s EmbeddingGemma 2 is an open multimodal embedding model that maps text, code, images, video, and audio into a shared 768-dimensional space, with a 270-million-parameter text pathway, an 8K-token context window, and support for Matryoshka Representation Learning, which allows vectors to be shortened without re-embedding data. Qdrant tested memory-saving approaches for its vectors across five text-retrieval datasets, comparing reduced dimensionality and 1-bit TurboQuant compression against exact 768-dimensional float32 search. Full-size 768-dimensional vectors quantized to 1 bit reduced vector RAM by roughly 30 times while retaining 99.0% of baseline retrieval quality without rescoring and 99.7% with rescoring, while a 256-dimensional 1-bit configuration with fourfold oversampling and rescoring used 77 times less vector RAM while retaining 94.5% quality. The tests suggest that retaining more dimensions while using stronger quantization generally performs better than reducing vector length first, and recommend beginning with full-size 1-bit vectors without rescoring before evaluating rescoring or dimensional reduction based on an application’s relevance, latency, and memory requirements.

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