---
type: "Topics"
locale: "en"
url: "https://longbridge.com/en/topics/40202168.md"
description: "$Alphabet - C(GOOG.US)If every Google investor is of this caliber, I suggest they first go get a degree in artificial intelligence before coming back to trade stocks.The direct target of TurboQuant is the KV Cache / vector representation memory overhead in LLM inference, not the memory in mobile phones.Google itself says it's about high-dimensional vector compression for large models and vector search. KV Cache can achieve about 3 bits, memory is reduced by at least 6 times, and running attention logits on H100 can be accelerated up to 8 times. Which part of that mentions phone memory? 😅The memory requirements of an AI system don't just come from KV Cache. Training, batching, parameter loading, activation, HBM bandwidth, distributed communication, data center storage, RAG indexing, logs, cold data—it's all still there.The cost per unit of inference decreases, but the total demand for inference may increase.This is the classic Jevons paradox: increased efficiency doesn't necessarily reduce total resource demand; it might instead cause usage to skyrocket."
datetime: "2026-04-26T02:56:23.000Z"
locales:
  - [en](https://longbridge.com/en/topics/40202168.md)
  - [zh-CN](https://longbridge.com/zh-CN/topics/40202168.md)
  - [zh-HK](https://longbridge.com/zh-HK/topics/40202168.md)
author: "[吉姆哈克的交易员](https://longbridge.com/en/profiles/10212142.md)"
---

# $Alphabet - C(GOOG.US)If every Google investor is …


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## Comments (2)

- **蜜桃猫的交易员Jeff · 2026-04-26T10:09:41.000Z**: All that effort and jargon in the end just proved my conclusion that "it doesn't affect storage sales," just a bunch of technical terms to show off...Whether memory stocks were bought or not is unknown, and Google doesn't show how long they've held it.Lesson one in stock trading: stay humble
- **蜜桃猫的交易员Jeff · 2026-04-26T09:54:15.000Z**: You don't even understand reading comprehension and you're already mocking? I suggest you go back and learn elementary school Chinese first. 🤣