Which one should you choose, DeepSeek all-in-one machine or Qwen3 all-in-one machine?

    Let me first state the conclusion: DeepSeek will be more involved in the 2C market, which is definitely the world of Moe; Qwen will definitely be involved in the 2B/2G market. Helping to maintain the market share of Alibaba Cloud is Qwen’s mission and responsibility, so Qwen must focus on the dense model. The two groups have different missions, different focus points, and different final technology choices. Of course, Internet companies all have 2C businesses and will do Moe, and the emphasis here is on the focus point.

    With the rapid development of large language model (LLM) technology, many excellent models and integrated hardware and software solutions based on them have emerged in the market. These all-in-one machines are designed to lower the threshold for enterprises and developers to deploy and apply large models. Among them, the DeepSeek series and the recent Qwen3 series have attracted much attention.

    When we need to choose between the DeepSeek all-in-one and the latest Qwen3 all-in-one, it is crucial to understand the differences in their core architectures.

    We focus on the impact of DeepSeek's MoE (Mixture of Experts) architecture and Qwen3's dense model architecture in the selection of all-in-one machines. (Qwen also has MoE, but we will not discuss this)

Core architectural differences: MoE vs. dense models

1. DeepSeek’s MoE (Mixture of Experts) architecture

• Higher inference computing power requirements
  Although only some experts are activated for each reasoning, the management and scheduling of these experts, as well as the calculations of the experts themselves, especially when multiple experts may be activated under complex queries, usually require higher computing resources (such as GPU memory and computing units). All-in-one machines need to be equipped with more powerful hardware to support their efficient operation.
• Complexity of training and fine-tuning
  The training and fine-tuning of MoE models are relatively complex and require more sophisticated strategies to balance the expert load and the optimization of the gating mechanism.

• Strong reasoning and thinking skills
  Since each expert can focus on solving a specific type of problem or learning knowledge in a specific field, the MoE model tends to perform better when dealing with complex tasks that require deep thinking and multi-faceted reasoning. It can be regarded as a "team of experts" that can mobilize resources in a targeted manner.
• Parameter scale benefits
  MoE allows the model to significantly increase the total number of parameters while maintaining (or even reducing) the amount of computation per inference, thereby improving the overall "knowledge capacity" and capability ceiling of the model.

• How it works
  The MoE model is not a single, huge neural network, but consists of multiple relatively small "expert networks" and a "gating network". When a request is input, the gating network determines which experts are best suited to handle the request, and then dynamically assigns the task to one or a few selected experts. This means that during the inference process, not all parameters of the model will be activated and used.
  
  

• Qwen3's Dense model architecture

• The upper limit of capability is strongly related to the parameter scale
  To improve the capabilities of dense models, it is usually necessary to directly increase the total number of its parameters, which will lead to a simultaneous increase in training and inference costs.
• Regularity and consistency
  For tasks that follow specific rules and have relatively fixed patterns (such as format conversion, following specific instructions, standardized question and answer, etc.), dense models tend to provide more stable and consistent outputs.
• Lower hallucinations
  Because all parameters work together, the output of a dense model is likely to be more "convergent" if it is well trained, and the probability of producing "hallucinations" that are inconsistent with the facts is relatively low.
• Fine-tuning friendly
  The structure of the dense model is relatively simple and direct. When performing domain-adaptive fine-tuning, it is easier to obtain ideal results and easier to control the fine-tuning process.
• Relatively low inference computing power requirements : Under similar "effective parameter" (referring to the parameters actually involved in a single calculation) scale, the inference process of dense models is usually more direct, and the instantaneous demand for computing power and scheduling complexity are lower than the MoE model.
• How it works
  Dense models are what we traditionally understand as neural networks. During inference, input data flows through all (or most) of the parameters in the network. All the "knowledge" of the model is encoded in the entire parameter matrix.
  
  

    DeepSeek all-in-one machine vs. Qwen3 all-in-one machine: We can choose the appropriate all-in-one machine according to specific needs:

    Don’t choose a large model all-in-one machine blindly! See which type of task you prefer, and then choose, don’t be impulsive!

Recommendations for selecting an all-in-one machine:

1. If your core needs are to handle highly complex tasks that require deep reasoning and creativity:

• For example: conducting scientific literature analysis, assisting in writing reports containing complex logic, generating diverse creative texts, solving open-ended problems, and conducting complex code-assisted development.
• Recommended choice: DeepSeek all-in-one machine.
   The "depth of thinking" and "breadth of knowledge" brought by its MoE architecture may be more in line with your requirements, but you need to ensure that the computing power of the all-in-one configuration can meet its needs.

• If your core requirement is to execute tasks with clear rules, high consistency requirements, or deep fine-tuning for specific areas:

• For example: building customer service robots, information extraction, standardized document generation, knowledge retrieval and question-answering in specific fields, content review, etc.
• Recommended choice: Qwen3 all-in-one machine.
   Its dense model architecture usually performs more stably on these tasks, has fewer hallucinations, and has relatively low cost and difficulty in fine-tuning, making it easier to achieve deep customization for specific business scenarios. At the same time, its computing power requirements are also more economical.

• Scenarios with strict requirements on computing power budget and operation and maintenance:

• If you want to achieve better overall performance within a limited computing budget, or have higher requirements for model stability and predictability.
• Recommended priority: Qwen3 all-in-one machine.
   Its lower inference computing power requirements and more mature fine-tuning ecosystem may have more advantages.

• There is a high demand for the upper limit of the model's "IQ" and sufficient budget:

• If you pursue the absolute upper limit of model capabilities, especially in solving cutting-edge, open-ended problems.
• Recommended for in-depth evaluation: DeepSeek All-in-One.
   However, it is necessary to fully evaluate its computing power support and adaptability to actual business scenarios.




• DeepSeek All-in-One
  With its MoE architecture, it performs outstandingly in handling complex tasks that require deep reasoning and creativity. It is a strong choice for pursuing the upper limit of model "intelligence", but usually requires stronger computing power support.
• The Qwen3 all-in-one machine has more advantages in regular tasks and specific field applications due to its dense model stability, low hallucinations and fine-tuning friendliness, and is more friendly to computing resources.
  
  

    The final choice should be based on your specific application scenario, task type, emphasis on model capabilities (reasoning, consistency, creativity), computing power budget and fine-tuning requirements.

    It is recommended that before making a final decision, if conditions permit , you should actually test and evaluate both types of models or all-in-one machines for your own typical tasks.

    With the continuous advancement of technology, the two types of architectures may learn from and integrate with each other, and future options may be more diverse.