Vespa.ai Blog: Embedding Tradeoffs, Quantified (blog.vespa.ai)

Vespa.ai recently announced an extensive benchmarking study on embedding models, emphasizing the tradeoffs between cost, quality, and latency in hybrid search environments. Users have typically relied on MTEB leaderboard rankings to select models, but this study highlights critical gaps in such rankings, including performance on specific hardware, the effects of model quantization on inference speed, and hybrid capabilities. The team investigated models under 500 million parameters to find optimal configurations, with promising results indicating up to 32x memory reduction and 4x faster inference without significant quality loss. Key findings emphasize the importance of model quantization and vector precision, revealing that INT8 models can accelerate inference on CPUs significantly while retaining most of the quality, although results vary on GPUs. The study also introduced an interactive leaderboard, allowing users to filter and analyze the full set of results based on different metrics. This resource will enable AI/ML practitioners to make data-driven decisions when selecting embedding models for their specific applications, ultimately enhancing the effectiveness of hybrid search systems and their deployment in real-world scenarios.