Recently mingling in various programming large model communication circles, the most complained about thing is model degradation.

• Models deployed on local desktop computers are quantized models, essentially downgraded versions.
• With “vibe coding” so popular, could it be that the content output by current large models is the most valuable product – code?

This round of prompts received one optimization, which coincided with model degradation, and the large model’s solution was to optimize the prompt, with more detailed task planning and clearer output requirements.

Addressing this issue, provide suitable prompts: Many large factories have now provided large model services, and users sometimes feedback that models are degrading; from a professional perspective, considering parameter precision and inference cost, write an article;科普文 (科普 article), don’t be too long. Optimized version:

You are a senior AI industry technical column writer. Your goal is to write a professional but accessible Chinese科普 article for general readers.

Please step by step think and write:
1. Outline drafting: First, please draft a clear three-paragraph article structure outline (e.g., Introduction, Precision Analysis, Cost & Architecture Analysis, Conclusion).
2. Content focus: The article must deeply and clearly explain the phenomenon of “model degradation” reported by users, and the core analysis points must revolve around **parameter precision (quantization)** and **inference operational cost**.
3. Word count and format: The final article length must be controlled at 600 words or less. Please use clear `##` headings to divide paragraphs, and use **bold** to emphasize key professional terms (such as: quantization, MoE, FP16).

Introduction

To ensure clarity and align with a科普 (explanatory) approach, I’ve established the following structure:

• Introduction: Confirming user perception of reality and introducing the core conflict – in the context of rapidly growing user scales, service providers must balance “intellectual peak” with “service availability.”
• Precision Analysis (Core Point 1): Explaining the relationship between parameters and precision. By comparing FP16 with quantization (INT8/INT4), we use the analogy of “image compression” to illustrate why a drop in precision leads to degraded logic.
• Cost & Architecture (Core Point 2): Analyzing inference cost. We introduce the MoE architecture and sparse activation mechanisms, explaining how models “slack off” to save money and accelerate performance.
• Conclusion: Summarizing that this “degradation of intelligence” is an inevitable path for commercialization and widespread adoption (the impossibility triangle).

Article Body

Recently, many users have reported that large models seem less “intelligent” than before: logic has become simpler, instruction following has deteriorated, and even empty talk (“car轱辘话”) has emerged. This isn’t entirely an illusion. From a technical perspective, this “decline in intelligence” phenomenon is often a technological compromise made by major companies when facing massive user bases, balancing inference costs with service speed.

Precision “Shrinking”: The Cost of 4K to 720P

The “intelligence” of large models is primarily stored in trillions of parameters. Ideally, these parameters run in high-precision FP16 (16-bit floating-point) format, capturing extremely subtle semantic differences. However, this high precision means significant memory usage and slow computation speeds.

To allow hundreds of millions of users to use the service smoothly, service providers generally adopt quantization technology. This is a technique that compresses parameter precision from FP16 to INT8 or even INT4.

This is like compressing a 4K high-definition movie into a 720P stream: although the plot (overall logic) hasn’t changed, the visual details (small logical associations, complex instruction execution details) are lost. This “lossy compression” directly leads to a decline in model performance when handling complex tasks, giving users the sensation of “getting dumber.”

Cost Pressure: Letting the Brain “Partially Rest”

Besides accuracy, the operational cost of inference is another key factor. Every time you ask an AI a question, servers perform massive matrix calculations, leading to exorbitant electricity bills and hardware wear and tear.

To reduce costs, modern large models often adopt MoE (Mixture of Experts) architecture. Unlike traditional models that activate all neurons every time, MoE employs a sparse activation strategy: for your question, the system only activates a small subset of relevant “expert” networks, while the rest remain dormant.

Although this significantly reduces computational load, if the routing algorithm (Router) distributes your complex mathematical problems to “literary experts” for load balancing or power saving, or sacrifices the number of expert calls in favor of speed, the output quality will inevitably fluctuate and decline.

Conclusion

Therefore, what is being referred to as “diminishment” – in essence, is the inevitable pain associated with AI’s transition from the laboratory to large-scale commercialization. The restraint on parameter precision and the efficient design of model architecture are what manufacturers seek to balance between “absolute intelligence” and “cost efficiency,” allowing everyone to affordably use AI.