Qdrant, a leading high-performance open-source vector database, is debuting its platform-independent GPU-accelerated vector indexing, solving indexing bottlenecks for large data use cases. Offering up to 10x faster index building and support for GPU acceleration across multiple platforms, Qdrant’s latest innovation makes it both faster and more cost efficient to build indexes for billions of vectors.
Unstructured data continues to rapidly grow, and many organizations are struggling to create indexes for their vast data estates. With billions of data points, the process of indexing is incredibly slow—from hours to even days, according to Andre Zayarni, Qdrant co-founder and CEO.
Qdrant’s new platform-independent GPU-accelerated vendor indexing—the first hardware-agnostic solution of its kind—meets enterprises in their unique stacks to accelerate their index building with scalability and cost efficiency in mind. By optimizing Hierarchical Navigable Small World (HNSW) index building—a traditionally resource-intensive set of steps in vector search pipelines—Qdrant empowers organizations with the necessary efficiency, adaptability, and simplicity in usage that an increasingly complex data world requires.
Qdrant’s platform-independent, hardware-agnostic design affords enterprises the flexibility to select the most suitable infrastructure for their needs while allowing them to process massive datasets. The platform-independent GPU-accelerated vendor indexing works seamlessly across any GPU architecture, including NVIDIA and AMD.
This release is particularly relevant to the AI era, where AI-powered applications necessitate real-time responsiveness, frequent reindexing, and the ability to make quick decisions on dynamic data streams, according to Qdrant. Platform-independent GPU-accelerated vendor indexing makes supporting these AI use cases—including live search, personalized recommendations, and AI agents—simplistic, effective, and resource efficient.
“We wanted to support any kind of hardware, so it's up to the user to choose what they want to use in their stack,” explained Zayarni. “We support not only NVIDIA or AMD but also Intel and Apple silicon…[it] can also run on local machines, if needed.”
Additionally, with Qdrant’s vector database open source availability, new capabilities are added as rapidly as AI technology changes, according to the company. This also delivers full visibility into Qdrant’s architecture, algorithms, and implementation.
To learn more about Qdrant, please visit https://qdrant.tech/.