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Milvus 2.5 vector database released with GPU acceleration for 10B+ vectors

By PulseAugur Editorial · [1 sources] ·

Milvus 2.5, an open-source vector database from Zilliz, has been released with enhanced capabilities for handling massive datasets. This new version features GPU-accelerated indexing, a distributed architecture, and tiered storage, enabling it to manage up to 10 billion vectors with millisecond latency. The database is designed for scalable similarity search and supports various index types, including those optimized for GPUs via NVIDIA RAFT, significantly speeding up index construction and query throughput. Milvus can be deployed from a simple Docker standalone setup to a production-ready Kubernetes cluster, with its commercial counterpart, Zilliz Cloud, offering a managed service with the same API. AI

IMPACT Accelerates AI applications requiring massive-scale similarity search, particularly in areas like recommendation systems and large-scale embedding management.

RANK_REASON New version of a significant open-source vector database with notable feature enhancements. [lever_c_demoted from frontier_release: ic=1 ai=0.7]

Read on dev.to — Claude Code tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Milvus 2.5 vector database released with GPU acceleration for 10B+ vectors

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Significant
New version of a significant open-source vector database with notable feature enhancements. [lever_c_demoted from frontier_release: ic=1 ai=0.7]
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
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High
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97 days old
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COVERAGE [1]

  1. dev.to — Claude Code tag TIER_1 English(EN) · Dibi8 ·

    Milvus/Zilliz 2026: The Vector Database Handling 10 Billion Vectors at Millisecond Latency — Deployment Guide

    <h2> Introduction: The Billion-Vector Problem </h2> <p>In late 2024, a mid-sized e-commerce company hit a wall. Their product catalog had grown to <strong>800 million items</strong>, each represented by a 1,536-dimensional embedding. Their existing vector search solution — a sing…

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