🚨 $GOOGL has just introduced a new technology called TurboQuant

🟢 What exactly is it?

TurboQuant is a language LLM model from Google that aims to solve a technical problem: huge demands on memory usage and speed.

When you communicate with an AI (e.g., via ChatGPT or Gemini), the model needs to "remember" the context of the entire conversation. This "memory" is stored in the so-called KV Cache.

🛑 But here's the problem: This memory is incredibly space-hungry. The longer your conversation (longer context), the more memory (VRAM) the graphics card needs.

🟢 How does it work?

Think of it as compressing the conversation data so intelligently that the model can still work with it, even though it takes up a fraction of the space.

👉 6x less memory: That means where you previously needed 60 GB of memory, you now only need 10 GB.

👉 8x higher speed: Because the data is smaller, the chip can process it much faster. So instant responses from the AI.

🟢 How else can Google's TurboQuant help us?

• AI directly on mobile: Thanks to this, you'll soon see top models running directly on your phone without needing the internet (Local AI Inference).

• Huge context: You'll be able to load an entire book or thousands of lines of code and the AI will "remember" them without running out of memory.

• Cheaper operation: For companies like Google this means operating AI will be much cheaper, which could lead to better free versions for users.

🚨 Memory card companies are under pressure today.