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1. The Buzz in the Large Model Community
In recent years, the large model community has been bustling with activity. Companies like OpenAI, Google, Meta, Anthropic, Microsoft, Alibaba, DeepSeek, and others have all launched or open-sourced various large models.
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3. The Details Behind the Buzz
A few days ago, while testing the Qwen3 series models, I noticed an interesting detail. In the minds of many ordinary people, the bigger the model, the stronger it is. I myself usually download the 7B, 14B, and 32B parameter versions.
But on the Qwen project page, I found that the downloads for the “mini” models like 0.6B and 1.7B were surprisingly high. My first reaction was that many people’s computer graphics cards aren’t powerful enough to support larger models.
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5. What Is the Real Significance of Mini Models?
In many scenarios, mini models are an effective solution to the “impossible triangle” of performance, timeliness, and concurrency, and are also an upgrade from traditional small NLP models.
When you search for products on an e-commerce platform, there may be tens of thousands of servers running frantically in the background. Imagine the scene during the The Black Friday shopping festival at midnight, with hundreds of thousands of people clicking the search box every second—such a massive online system must operate precisely every second, and what really drives it is often not huge AI models, but these “little guys.”
These systems have extremely demanding speed requirements. It’s like a highway toll booth: if every car had to recite an essay before passing, the whole intersection would immediately be jammed. In search and recommendation scenarios, each user request needs to be processed within 10 milliseconds, while handling tens of thousands of queries per second. In this case, let alone using a 7-billion-parameter model—even a 700-million-parameter model would make the servers “go on strike,” and the hardware and electricity bills would skyrocket.
This is where 60-million-parameter micro models come into play. They’re like efficient sorters on an assembly line—not as knowledgeable as a university professor, but especially good at quickly completing specific tasks: instantly correcting typos like “applle first phone” to “Apple smartphone,” inferring that “want to buy a pair of lightweight sneakers” might actually mean hiking shoes, or filtering 500 relevant products from 100,000 in just 0.01 seconds. These jobs don’t require deep thinking; the key is fast and stable response.
More realistically, the computational load in many scenarios isn’t just one-to-one. For example, when you search for “gifts suitable for a girlfriend,” the system has to match your query against millions of products—essentially doing millions of inferences. Using a large model here would be like asking every courier to deliver small packages in a truck, while a micro model is like a courier on an e-bike: each trip is small, but the volume and turnover are high.
The power of these micro AIs lies in their “family background.” Though tiny, they inherit advanced architectures from large models like GPT—like building a small e-bike with sports car technology. Rotary position encoding helps them understand sentences more precisely, KV cache mechanisms speed up continuous inference, and decoder structures make real-time responses smoother. Plus, they’re trained on internet-scale data, so even though they’re only a tenth the size of traditional BERT models, they’re actually smarter in real-world tasks.
Most importantly, these models have a clear self-positioning: they’re not the decision-making brain, just intelligent assistants. In product ranking systems, they don’t need to judge which product is best; they just need to add extra signals like “this product title contains the user’s search keyword” or “that detail page was recently clicked frequently” on top of traditional algorithms. This can boost the overall recommendation effect by several percentage points—like a sprinkle of scallions at the end of a dish: small in quantity, but it makes the whole dish more fragrant.
The real value of these micro AIs is that they add “perceptive tentacles” to systems at extremely low cost. While other teams are showing off trillion-parameter models, the ones really carrying the metrics on the production line are often these unsung, ubiquitous small models. Their daily performance—handling tens of billions of requests—proves that in industrial-scale systems, sometimes “good enough” wisdom is far better than blindly joining the parameter arms race.
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7. Insights for Ordinary Users
Now, many ordinary users are exploring large model applications, and I think these “small models” are a very promising direction. Big companies can afford tens of thousands of GPUs, but most people can’t. By leveraging the low hardware requirements of small models, ordinary users can also fine-tune them to efficiently handle problems in certain niche areas.
