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New disk-based vector database AsterVec uses less RAM for local AI

By PulseAugur Editorial · [1 sources] ·

The developer of AsterVec has created a new disk-based vector database designed to operate within a configurable memory budget, addressing the RAM competition between local LLMs and embedded vector stores. Unlike traditional in-memory or RAM-dependent vector databases, AsterVec separates its index into two disk-optimized structures: an LSM-tree for graph edges and a page-based array for vectors, with only the upper navigation layers and caches residing in RAM. Benchmarks on the SIFT dataset show AsterVec significantly outperforming Chroma and LanceDB in query and insert speeds while using substantially less memory, making it suitable for local AI applications with memory constraints. AI

IMPACT Enables running larger local LLMs and RAG systems by reducing the memory footprint of vector databases.

RANK_REASON New software tool release for AI infrastructure.

Read on dev.to — LLM tag →

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

New disk-based vector database AsterVec uses less RAM for local AI

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25 / 100
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New software tool release for AI infrastructure.
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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Volatil ·

    Why I Built a Vector Database Around Disk Instead of RAM

    <p>I run my AI stack locally: the LLM, the embedding model, and the vector store behind RAG and the agent's memory. Everything shares one machine's RAM. The vector store's share is the part that grows. An embedded vector store keeps its entire index in memory, its memory usage sc…

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