Findings
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[1] 2026-09-25 GPU-accelerated vector indexing in Elasticsearch with NVIDIA cuVS: 138M vectors in under 10 minutes
Modern enterprise applications are ingesting terabyte- to petabyte-scale unstructured data to power semantic search, large language model–based (LLM-based) retrieval augmented generation (RAG), and recommender systems. At this scale, vector indexing on CPUs can take days or even weeks, slowing experimentation and making large index updates operationally expensive. This is especially challenging for workloads that require regular index rebuilds, such as…
Where Kimbodo Comes In
Kimbodo builds and operates this in production for businesses — see our RAG Development Services practice, or Estimate My RAG System.
