qdrant-memory-usage-optimization
Diagnoses and reduces Qdrant memory usage. Use when someone reports 'memory too high', 'RAM keeps growing', 'node crashed', 'out of memory', 'memory leak', or asks 'why is memory usage so high?', 'how to reduce RAM?'. Also use when memory doesn't match calculations, quantization didn't help, or nodes crash during recovery.
Install
With your agent
Install OMGSkills skill "qdrant/skills:skills/qdrant-performance-optimization/memory-usage-optimization" into this agent's normal skills directory: call the OMGSkills MCP get_install_plan tool for your agent and run the script it returns (it installs only the pinned commit and verifies SKILL.md), and stop if MCP or the pin is unavailable. Do not overwrite or change existing skills without asking me.
Manual command
git clone https://github.com/qdrant/skills /tmp/skills && ln -s /tmp/skills/skills/qdrant-performance-optimization/memory-usage-optimization ~/.claude/skills/qdrant-memory-usage-optimizationFrom README
Understanding memory Qdrant operates with two types of memory: Resident memory (aka RSSAnon) - memory used for internal data structures like the ID tracker, plus components that stay fully in RAM. As of Qdrant 1.20 the ID tracker has its own memory tier (default pinned; cached lets the OS evict it, cold needs no up-front RAM), where it was always resident before. On Qdrant 1.19 or newer this is controlled per-component with memory: pinned (e.g. quantized vectors, payload indexes); see memory tier legacy settings for deployments on version 1.18 or older. OS page cache - memory used for caching disk reads, which can be released when needed. Original vectors are normally stored in page cache, so the service won't crash if RAM is full, but performance may degrade.