32B is enough for QwQ? Llama4 needs 2000B?!

    First of all, the conclusion is: the larger the parameters, the stronger the model capability. This conclusion continues to be useful. The scaling law has not become invalid, and stacking parameters can still improve the model capability.

    In the field of AI, the "arms race" of model parameters seems to be endless. R2, GPT-5, Qwen3, and Wenxin-5 will be launched soon, with the focus on multi-modality. Multi-modality calculations are more demanding, and low-precision mixed training is more important...

    As for what model to choose and how many parameters, it is becoming more and more interesting. We think moe is suitable for chat scenarios, and fine-tuning still gives priority to dense models, which are easy to align. Moe alignment is "disgusting, it's disgusting, it's disgusting to the extreme", the workload is too large, and the technical difficulty is too high!

    On one side is Alibaba's QWQ-32B, which challenges industry giants with 32 billion parameters;

    On the other side is Meta's Llama4, which launched the "behemoth" Behemoth with 2 trillion parameters.

    Why do some people think that "small parameters are enough" while others pursue "parameter explosion"?

    The basic principle is that if it is a chat assistant requirement, the larger the model parameters, the better, because everyone usually experiences large parameters. If a small parameter is deployed privately on the intranet, everyone will definitely not experience it well, especially the leader's experience "He was fired because of the poor experience of choosing DeepSeek 70B". The chat requirement is very embarrassing. Once the leader has experienced the high IQ of the public network, it is difficult for him to experience a bad one again!

    For other tasks or fine-tuning, 32B may be enough for convenience!

   Swiss Army Knife: QWQ-32B, small but complete

    1. Reinforcement learning (RL) training: It is like a "problem solver". Through repeated training of math problems and code tests, it uses the result feedback to optimize its reasoning ability, and finally it can match DeepSeek-R1 with larger parameters in math (AIME24) and programming (LiveCodeBench) tasks.  

     2. Precise quantization technology: Through 4-bit quantization (Q4_K_M), the video memory occupies only 22GB, and a modified 2080Ti graphics card can run it, which can be called "good news for consumer-grade graphics cards."  

    Small parameters are suitable for business scenarios with strong rules and the more constraints the better, such as code generation, math problem solving, and lightweight conversational assistants - just like a flexible electric donkey that can travel through the streets without any pressure.

    It's like a Swiss Army knife, which can do everything but isn't very strong in anything, or it's easy to enhance professional skills and fine-tune them.

  Starship faction: Llama4 Behemoth, the ambition of the behemoth, is an awesome teacher model, mainly used to distill student models. The parameters have soared to 2 trillion. The goal of Behemoth is not only to "solve problems", but to solve all problems encountered.

    It natively supports early integration of text, images, and videos, processes 8 images at a time, and its visual reasoning is as accurate as that of an eagle eye.  

    As a teacher model, the "mentor" of distillation technology compresses and transmits knowledge through co-distillation technology, driving the overall evolution of the family. Scientific research computing, cross-modal content generation, enterprise-level complex systems - just like a supercomputer, specializing in "high-precision" problems.

        You get what you pay for. It depends on the scene:

    If you have a chat assistant business, it is strongly recommended that the parameter model be as large as possible. As long as the hardware cost can be sustained, an infinite model is fine.

    If it is a fine-tuning industry model, try not to choose moe. Moe fine-tuning alignment is a disaster movie. It is best to choose a dense model and do your own fine-tuning training based on industry data. You should choose a model for your own industry.

    Recently, we have encountered several cases where integrators used open source software to fine-tune the moe 671B model for customers. After the adjustment, the effect was not as good as the original one. They found us and we gave them two options: 0-cost plan, directly use the original version, or accept our quotation plan. In the end, the budget was not enough and the matter was left unresolved.

    moe 671B fine-tuning is extremely expensive, not only because of the computing power but also because of the people involved. All of us who do this are PhDs and postdocs who graduated from the Chinese Academy of Sciences, Tsinghua University and Peking University. It is extremely expensive!

    Sometimes, some businesses are like a newborn calf that is not afraid of a tiger, and it is a blessing to suffer losses! !        

    Summary Remember:

    If you want to save trouble, don't do fine-tuning, choose parameters as large as possible, go directly to native applications, and directly make peripheral applications, such as RAG. The effect is good, the difficulty is low, and the results are immediate!

    If you want to fine-tune, try not to choose moe. Choose a dense model with as small parameters as possible (provided that the requirements are met). It is easy to align and not prone to failure!

    A technical team that can make fine adjustments and alignments based on 671B and produce very good results has incredible technical strength. Don’t even think about it if the project amount is less than 1000w!