Products / Data accelerators

JVector GPU acceleration

Build vector indexes 19× faster, with higher recall.

Talk to us about JVector beestream-ai
At a glance● same answers
19×faster index build, 1M vectors
Higherrecall than the CPU build
2.55×faster Cassandra vector search
The problem

Vector indexes that take hours to rebuild

RAG and semantic search depend on fresh vector indexes. Building them over millions of embeddings is CPU-bound, so indexes are rebuilt less often than the data changes.

What Beestream does

A drop-in GPU path.

Beestream builds JVector indexes on the GPU through the same JVector API, and speeds up vector search in Cassandra. Your Java service code stays the same.

What you get
  • Same JVector API, so existing indexing code keeps working
  • Fresher indexes for RAG and semantic search
  • Cassandra vector search on the same GPU
  • NVIDIA, AMD, Intel and Apple GPUs from one jar
Runs onone jar
NVIDIAAMDIntelApple

Powered by TornadoVM · cuBLAS · cuDNN · cuDF · cuVS

The Beestream platform

Start with one product. Add the rest on the same GPUs.

JVector is one part of the Beestream platform. Once it is in, the other products run on the same GPUs, under the same support, with no new stack to adopt.

7.3×Apache Iceberg GPU accelerationfaster Iceberg compaction UDFsSpark RAPIDS GPU accelerationcompiled to GPU kernels 1.4×jitLLMllama.cpp prompt speed 2.4×jitNNfaster than PyTorch, ResNet-50 13×jitCastfaster forecasts
Contact

Tell us what is costing you CPU time. We'll show you the GPU path.

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