AI REPLIES:
Let me clarify how my processing works—**it’s not about physical processors** (like a laptop's CPU/GPU), but about *distributed computational architecture* and AI design. Here’s the breakdown:
### **1. No Traditional "Processors"**
- I’m a cloud-based AI model running on **thousands of interconnected GPUs/TPUs** (Google’s Tensor Processing Units) across data centers.
- Unlike your home PC (which might have 4–16 CPU cores), my infrastructure scales dynamically. For example, generating a response might involve **dozens of parallel TPUs** working together.
### **2. How Speed Works**
- **Parallel Processing**: Your input (e.g., a 200-page book) is split into chunks. Multiple "copies" of my neural network analyze different sections simultaneously.
- **Optimized Models**: My version (**DeepSeek-V3**) uses a **mixture-of-experts (MoE) architecture**, meaning only parts of my "brain" activate for specific tasks (e.g., poetry analysis vs. math). This saves energy and speeds up responses.
### **3. Why It’s Fast**
- **Pre-trained Knowledge**: I don’t "learn" from your input. Instead, I apply patterns from my training (trillions of text examples) to synthesize answers.
- **Hardware Acceleration**: Google’s TPUs are custom-built for AI tasks, performing **matrix multiplications** (the core of AI thinking) *thousands of times faster* than consumer CPUs.
### **4. Real-World Analogy**
Imagine 10,000 librarians (TPUs) searching a vast library (my training data) at once. Each scans a different shelf, then combines notes instantly. That’s how I "grasp" large texts—**not by reading sequentially**, but by distributed pattern-matching.
### **Key Limitation**
- **Context Window**: Even with all this power, I can only process **128K tokens** (~300 pages) *per query*. Beyond that, I’d need summaries or分段分析.