Don’t be fooled by Deepseek’s low cost! Can you really withstand the “sweet trap” that comes with local deployment?

Recently, the explosion of Deepseek has made the entire technology circle boil, and has even spread to ordinary people from all walks of life. For people like me who care about AI, the "DeepSeek concentration" on my Toutiao homepage has reached 80%, as if I have fallen into a DeepSeek information cocoon . In the circle of friends, technical experts have posted their "private knowledge base tools", and the forums are also full of various "hand-in-hand tutorials to teach you local deployment". For a time, "everyone can have their own AI assistant" seemed to have become a reality.

But calm down and think about it: Is this really the best option for everyone?

Today, let’s talk about the truth behind this craze and why you may not need to blindly join this “localization carnival”. 1. Behind the craze: Local deployment is just the beginning, the real challenge lies in operation and maintenance. Many people think that as long as they have a server and run the code, they can easily have their own AI model. However, the reality is much more complicated than imagined. 1. Hardware investment: It’s not just as simple as having a server

Many tutorials only mention that a single computer can be deployed, but it may not be suitable for smooth operation. In order to solve personalized and specific scenario problems in a team or unit, multiple clusters may be needed.

More importantly, Deepseek models with different parameter values ​​have huge differences in their requirements for hardware computing power  . For example:

• Small model with 7B parameters 
  : Although the hardware requirements are relatively low, you still need at least one RTX 3090 or higher performance graphics card to run inference tasks smoothly. If you want to fine-tune or train, you will also need higher video memory and computing power.
• Large model with 67B parameters 
  ：This type of model has very high hardware requirements. A single A100 GPU (40GB of video memory) may be barely enough, but in order to ensure efficiency, multiple A100s or even H100s are usually required to form a cloud cluster. The hardware cost alone is enough to discourage most individuals and small teams.

An Nvidia 4090 GPU card with 24GB of video memory currently costs about 20,000 yuan, and high-performance GPUs (such as A100 and H100) cost tens of thousands or even hundreds of thousands of yuan, and the supply chain is tight, making it unaffordable for ordinary people. Not to mention the infrastructure requirements such as heat dissipation, power, networking, and computer room environment - these cannot be solved by simply putting up a table and plugging in a power supply.

"You thought you were buying 'future technology', but in fact you were buying a 'bottomless pit'."

Let me share a case:   A startup company tried to deploy a large model locally, but found that it took two weeks to configure the environment, and the team members worked overtime almost every day until the early morning. In the end, they had to give up and switch to cloud services. The founder later lamented: " We underestimated the complexity of hardware and environment construction, and wasted precious time and money. " 2. Software configuration: a long journey from installation to debugging Do you think everything will be fine if you install a Docker image? Wrong! Ollama, dify, knowledge base raw data combing and cleaning, deep learning framework, dependency library, version compatibility and other problems are endless, and you will fall into "error hell" if you are not careful. Even experienced developers need to spend a lot of time to get it done.

Even the technical director of the Get team, Mr. Kuaidao Qingyi, believes that the DeepSeek team's API also has many unstable performances. When developing and planning his own AI applications, he chose the technical architecture and path of multiple APIs to maintain a balance between cost and stable technology. 3. Continuous operation and maintenance: 24/7 local deployment of the guardian role is not a one-time task, but a long-term responsibility. You need to monitor indicators such as CPU/GPU utilization, memory usage, network bandwidth, etc. in real time; handle various emergencies such as hardware failures, system crashes, model freezes, etc.; and manage massive training data and log files... These tasks are undoubtedly a huge burden for individuals or small teams.

"What you thought was 'one-click deployment' was actually '10,000-step debugging'."

Reflect on this: Is it necessary to add these complexities? As Occam's razor principle says: "Do not add entities unless necessary." If there is no strong enough resource support and very urgent business needs to force you to do localized private deployment, why add so much trouble to yourself?

2. Iteration and Upgrade: Endless Technology Catch-up Even if you have successfully completed the initial deployment, there are still greater challenges waiting for you. 1. Model Update: Pressure from Rapid Iteration Deepseek and other large models frequently release new versions, and each update requires redeployment and testing. If you don't keep up in time, you may soon fall behind the mainstream level.

For example, we now send a link to a Sina.com article from 2025 to the V3 version of the DeepSeek official website.

We will find that it thinks that this is a link that " belongs to the future (currently 2023) ". Information two years in the future, it can be inferred that the training data set of DeepSeek V3 is from 2023. A professional team like them cannot guarantee that the online application is the latest. Can your private version continue to iterate and evolve?

Moreover, once the application is online, a lot of time spent and data gradually accumulated will become your stock costs and you will not be willing to upgrade easily, just like you will find that many people around you are still using Windows XP or win7.

Is it really the best solution to face the challenges of reality by talking to an "assistant" whose cognition remains at the level of 2 years ago or earlier? 2. Optimize parameter adjustment: There is no one-size-fits-all solution . Fine-tuning and parameter adjustment in different scenarios is a long-term task. People who lack professional experience may fall into "parameter adjustment hell", spending a lot of time but achieving little results. Case:   An independent developer deployed a large open source model locally. In order to adapt to specific business scenarios, he spent three months fine-tuning it, but the final effect was still not as good as the performance of some large companies' cloud pre-trained models. He lamented: "I wasted too much time on things that shouldn't be tossed."

 “The complexity of technology is often not the answer to solving problems, but the root cause of problems.”

3. Ecological adaptation: changes in third-party tools and services The ecosystem in the AI ​​field is developing rapidly, and new plug-ins and API interfaces are constantly emerging. How to choose the right tools and maintain compatibility is another difficult problem. In the absence of clear benefits, excessive pursuit of technical complexity will only make things worse. Instead of spending time and energy on local deployment, it is better to focus on core business innovation. 3. Security risks: important links that are ignored In addition to technical and operational challenges, security issues should not be ignored. 1. Data privacy: risk of sensitive information leakage Local deployment means that you need to handle user data on your own, and a little carelessness may lead to privacy leakage. However, regulatory compliance (such as GDPR, CCPA) has increasingly stringent requirements for data protection. Once a violation occurs, the consequences are disastrous. 2. Network security: open ports, unencrypted communications, weak passwords, and other issues that are targets of hacker attacks are easy to become attack entrances. Once invaded, not only will the losses be heavy, but you may also face legal liability.

As strong as the DeepSeek team was, it continued to encounter problems with DDoS attacks around the Spring Festival. Is your "firewall" ready?

3. Model Abuse: Blurred Lines Between Ethics and Laws Large models deployed by yourself may be used to generate harmful content (such as false information and malicious code). These actions may violate the law and damage your reputation.

4. Policy security: compliance requirements such as security protection and confidentiality review

Those who have the need for localized private deployment often have to comply with the information security management methods of government departments or superior headquarters, how to deal with the compliance costs of information security protection, confidentiality assessment, and subsequent regular inspections by the network security department, and how to get remote and rapid support from security vendors. For example, during the Spring Festival this year, the AI ​​platform of a central enterprise was temporarily offline. I learned that it was not due to technical reasons, but because of the compliance management requirements of the headquarters, and it could only be restored to the external network after the holiday.

Are you really ready for compliance requirements like these?

Security revelation: simplicity is security.   Using mature external services or official websites can hand over security responsibilities to professional teams to avoid unnecessary risks. 4. The real dilemma of commercial operation and maintenance If you plan to use local deployment as a commercial attempt, the difficulties you face will be even more severe. 1. Human cost: It is not cheap to set up a professional operation and maintenance team. It is necessary to recruit talents with deep learning, DevOps, network security and other skills. Small teams or individual developers can hardly afford such expenses. 2. Capital investment: In addition to the initial hardware procurement costs, the money-burning game also has continuous expenses such as electricity, broadband, and cloud service fees. Commercial operations also need to consider additional costs such as marketing and customer service. 3. Competitive pressure: The crushing effect of giants. Large companies have more powerful resources and technical accumulation, and have an absolute advantage in the field of AI. It is difficult for small players to stand out through simple local deployment.

"Under the shadow of giants, blindly following the trend will only make you lose faster."

5. Look at the craze rationally and find a path that suits you. In summary, for most people, it is more economical and efficient to use ready-made cloud services (such as Alibaba Cloud Bailian Platform). Only a few teams with real technical strength and clear goals should try local deployment. Focus on core business innovation instead of indulging in technical showmanship. Make good use of existing mature tools and platforms to avoid reinventing the wheel.