---
title: "Google's Algorithm Reduces Memory Usage with TurboVec"
url: https://stacklist.com/card/0d63387d-ec86-4a63-9889-d6de5eb36513
source_url: "https://x.com/dr_cintas/status/2062941225133510658"
stack: https://stacklist.com/stack/4aae218c-38d7-4c05-b7ae-f2db0029a2e8
summary: "Google's TurboVec is a new open-source tool that reduces memory usage for AI application vector storage by 16x, shrinking 31GB down to 4GB using the TurboQuant quantization algorithm. It offers a faster alternative to FAISS, runs fully offline, supports Mac and standard servers, and integrates directly with LangChain and LlamaIndex."
tags: "turbovec, memory-optimization, vector-search, open-source, google, ai-infrastructure, quantization"
key_entities: "Google (organization), TurboVec (technology), TurboQuant (technology), FAISS (technology), LangChain (technology), LlamaIndex (technology), Alvaro Cintas (person), Python (technology)"
classification: "snippet"
content_hash: "sha256:7e843b3e79a8424f5c0dd2797dfd0e3e4be0ca1d8a1471f8670f4eb9f66f63b5"
acp_version: "0.2"
token_counts_approximate: 697
visibility: public
agent_accessible: true
status: "final"
---

# Google's Algorithm Reduces Memory Usage with TurboVec

Alvaro Cintas @dr_cintas Google's new algorithm just shrunk 31GB of memory down to 4GB 🤯 TurboVec is a new open-source tool that stores the data your AI app searches through, using 16x less memory. It runs on Google's TurboQuant, which skips the slow setup step every other tool needs. → Faster alternative (FAISS) → Works on both Mac and standard servers → Narrow results to exactly what you want → Plugs straight into LangChain and LlamaIndex Your data never leaves your machine. Runs fully offline, works with Python out of the box. 100% Open Source. 4:54 PM · Jun 5, 2026 148.3K Views :host{display:inline-block;direction:ltr;white-space:nowrap;line-height:var(--number-flow-char-height, 1em) !important}span{display:inline-block}:host([data-will-change]) span{will-change:transform}.number,.digit{padding:round(nearest, calc(var(--number-flow-mask-height, 0.25em) / 2), 1px) 0}.symbol{white-space:pre} 5 7 number-flow-react > span{font-kerning:none;display:inline-block;line-height:var(--number-flow-char-height, 1em) !important;padding:calc(round(nearest, calc(var(--number-flow-mask-height, 0.25em) / 2), 1px) * 2) 0} 57 :host{display:inline-block;direction:ltr;white-space:nowrap;line-height:var(--number-flow-char-height, 1em) !important}span{display:inline-block}:host([data-will-change]) span{will-change:transform}.number,.digit{padding:round(nearest, calc(var(--number-flow-mask-height, 0.25em) / 2), 1px) 0}.symbol{white-space:pre} 3 4 3 number-flow-react > span{font-kerning:none;display:inline-block;line-height:var(--number-flow-char-height, 1em) !important;padding:calc(round(nearest, calc(var(--number-flow-mask-height, 0.25em) / 2), 1px) * 2) 0} 343 :host{display:inline-block;direction:ltr;white-space:nowrap;line-height:var(--number-flow-char-height, 1em) !important}span{display:inline-block}:host([data-will-change]) span{will-change:transform}.number,.digit{padding:round(nearest, calc(var(--number-flow-mask-height, 0.25em) / 2), 1px) 0}.symbol{white-space:pre} 2 . 6 K number-flow-react > span{font-kerning:none;display:inline-block;line-height:var(--number-flow-char-height, 1em) !important;padding:calc(round(nearest, calc(var(--number-flow-mask-height, 0.25em) / 2), 1px) * 2) 0} 2.6K :host{display:inline-block;direction:ltr;white-space:nowrap;line-height:var(--number-flow-char-height, 1em) !important}span{display:inline-block}:host([data-will-change]) span{will-change:transform}.number,.digit{padding:round(nearest, calc(var(--number-flow-mask-height, 0.25em) / 2), 1px) 0}.symbol{white-space:pre} 3 . 2 K number-flow-react > span{font-kerning:none;display:inline-block;line-height:var(--number-flow-char-height, 1em) !important;padding:calc(round(nearest, calc(var(--number-flow-mask-height, 0.25em) / 2), 1px) * 2) 0} 3.2K Read 57 replies