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How to cut vector search memory with quantization with Qdrant

Turn on built-in quantization and on-disk storage so a big collection fits on a smaller machine.

Short answer: Run Qdrant and load your collection; then enable vector quantization; the README says it cuts RAM usage by up to 97%; Move data to on-disk storage where memory is tight. All steps.

Who it is forA startup whose vector index is outgrowing the RAM of the server it can afford.

The problemMillions of full-precision vectors need a lot of memory, and bigger servers cost more every month.

The outcomeA collection that uses far less RAM, with a speed and precision trade-off you tune yourself.

What you need first

  • Docker for the local server, or a free tier cluster on Qdrant Cloud
  • A client library, for example the Python client
  • Embeddings from a model of your choice

Steps

  1. Run Qdrant and load your collection
  2. Enable vector quantization; the README says it cuts RAM usage by up to 97%
  3. Move data to on-disk storage where memory is tight
  4. Measure recall on a sample of known queries before and after
  5. Scale out later with sharding and replication if one node is not enough

Prompt for your agent

For my qdrant/qdrant collection <name>, enable quantization and on-disk storage, then measure memory use and top 10 recall on <sample queries> before and after. Recommend settings that keep recall above <target>.

Caveat. Quantization trades precision for memory, so always check recall on your own data.

Use this in your AI agent

Ask your agent

Using the Deduped MCP, get the setup steps for qdrant/qdrant and "Cut vector search memory with quantization"

Add Deduped to Claude Code once

claude mcp add --transport http deduped https://dedupedai.com/mcp

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