The Manticore Search team rebuilt its ONNX path for embeddings, resulting in a 14x speed increase [1].
This optimization reduces the computational overhead required to process embeddings, which are critical for modern vector search, and semantic retrieval. By increasing the throughput of these operations, the platform can handle larger datasets and more complex queries with lower latency.
Engineers focused on the ONNX path to eliminate bottlenecks in how the system handles embeddings processing. The transition to the new architecture allows the search engine to execute these tasks more efficiently, a change that directly impacts the responsiveness of the search platform.
"We've been working on optimizing our ONNX path for embeddings, and we’re thrilled with the results," the Manticore Search team said [1].
The update is part of a broader effort to enhance the technical infrastructure of the platform. According to the team, the performance jump is not merely an incremental gain but a fundamental shift in how the system manages the embedding pipeline.
"This represents a significant step forward in our commitment to providing high-performance search solutions," the Manticore Search team said [1].
The technical details of the rebuild were shared in a blog post published Oct. 26, 2023 [1]. The development has since drawn attention from the developer community, including 47 points of engagement on Hacker News [2].
“Manticore Search rebuilt its ONNX path for embeddings, resulting in a 14x speed increase.”
The significant speed increase in ONNX processing indicates a shift toward more efficient vector search capabilities. As AI-driven search relies heavily on embeddings to understand context rather than just keywords, reducing the latency of these paths allows for real-time semantic search at a scale that was previously computationally expensive.
