Qdrant Launches the First Platform-Independent GPU-Accelerated Vector Indexing for Real-Time AI Applications
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Qdrant, the leading high-performance open-source vector database, today introduced its platform-independent GPU-accelerated vector indexing. This new feature delivers up to 10x faster index-building times while leveraging cost-effective GPUs that rival or surpass CPUs in both cost and efficiency. With support for GPU acceleration across multiple platforms, Qdrant empowers developers to scale real-time AI applications flexibly and free from hardware vendor constraints.
The GPU-accelera ted feature optimizes HNSW (Hierarchical Navigable Small World) index building, one of the most resource-intensive steps in the vector search pipeline — particularly when scaling to billions of vectors. As the first hardware-agnostic solution of its kind, Qdrant works seamlessly across any GPU architecture — including NVIDIA and AMD — allowing users to choose the most cost-effective hardware while enabling faster index-building and efficient scaling.
Accelerating Real-Time AI for Context-Rich, Dynamic Applications
“Index building is often a bottleneck for scaling vector search applications,” said Andrey Vasnetsov, Qdrant CTO and Co-Founder. “By introducing platform-independent GPU acceleration, we’ve made it faster and more cost-effective to build indices for billions of vectors while giving users the flexibility to choose the hardware that best suits their needs.”
Building on Qdrant’s proven capabilities, this release unlocks new possibilities for AI-powered applications — such as live search, personalized recommendations, and AI agents — that demand real-time responsiveness, frequent reindexing, and the ability to make immediate decisions on dynamic data streams.
Qdrant’s hardware-agnostic approach to GPU acceleration, unique in the market, enables users to speed index-building while ensuring seamless scalability and cost efficiency. Support for most modern GPUs gives organizations the flexibility to efficiently process massive datasets while adopting and using the most suitable infrastructure for their real-time AI applications based on technical, cost, and other considerations.
The hardware agnosticism of the GPU-accelerated vector index feature builds on the flexibility the Qdrant platform provides — and enterprises require. The Qdrant vector database is open source, enabling new capabilities to be added as quickly as AI technology evolves and providing full transparency into the platform’s architecture, algorithms, and implementation. In addition, the Qdrant Hybrid Cloud option can be deployed in customers’ chosen environments without sacrificing the benefits of a managed cloud service.
Learn more about the announcement here: qdrant.tech/articles/
About Qdrant
Qdrant is the leading, high-performance, scalable, open-source vector database and search engine, essential for building the next generation of AI/ML applications. Qdrant is able to handle billions of vectors, supports the matching of semantically complex objects, and is implemented in Rust for performance, memory safety, and scale. Recently, Qdrant’s open-source project surpassed 10 million installs and earned a place in The Forrester Wave™: Vector Databases, Q3 2024. The company was also recognized as one of Europe’s top 10 startups in Sifted’s 2024 B2B SaaS Rising 100, an annual ranking of the most promising B2B SaaS companies valued under $1 billion.
View source version on businesswire.com: https://www.businesswire.com/news/home/20250123485958/en/
