Logic of DeepSeek All-in-One

Written by

Jasper Cole

Updated on:July-12th-2025

After DeepSeek came out, many hardware and integration manufacturers have been promoting DeepSeek large model all-in-one machines. Regarding the concept of DeepSeek all-in-one machines, many experts think that this is a waste of money, and many practitioners believe that all-in-one machines can help many traditional companies quickly lower the threshold for the implementation of large AI models in their companies.

Is it worth buying an all-in-one machine that can run DeepSeek 671B large models at full capacity, such as an Nvidia H20 machine with 8 cards and 141G video memory, with a minimum cost of more than one million yuan? Today we will talk about the logic behind the DeepSeek all-in-one machine.

1. A billion-dollar achievement can now be achieved with just a few million dollars

In 2025, thanks to the emergence of DeepSeek, the large model that OpenAI spent tens of billions of dollars to build can now be implemented within enterprises at a cost of more than one million. A single eight-card machine can run the full-powered version of DeepSeek, and such an architecture is also very easy to maintain for large enterprises.

Many large enterprises, especially those within the system, have been slow to implement AI in the past due to the need for data security protection, because the capabilities of open source large models that are not suitable for local use are very different from those of top commercial large models. Internal application scenarios cannot even be implemented with a POC verification. Many imagined scenarios have unsatisfactory results after running with open source 7B models, which will make departments or employees who are passionate about AI lose confidence.

Today, the cost of DeepSeek all-in-one machines is affordable for many large companies, so they can quickly verify various sensitive scenarios within the company using large models. Whether the large model is right or wrong, these issues will not be spread to the outside world, will not cause social impact, and can reduce the psychological burden of corporate innovation.

Many large companies, especially those within the system, have extremely strict requirements for data security protection, especially when it comes to data outbound, such as connecting internal application scenarios with OpenAI's large model API. In this regard, China's requirements are basically consistent with those of European and American countries. After DeepSeek came out, many European and American countries also restricted local companies from connecting to large models abroad.

In the past, the implementation of AI was extremely slow, and the open source large models adapted for local deployment were far behind the top commercial large models such as OpenAI in terms of capabilities. For some internal application scenarios, it was difficult to do a POC verification. Many of the envisioned scenarios, after running with the open source 7B model, had unsatisfactory results, which undoubtedly poured cold water on the departments and employees who were originally enthusiastic about AI and frustrated their confidence. Every day, everyone is watching how well and smoothly other advanced companies use AI; in contrast, the results of AI practice are unsatisfactory, which makes people wonder whether the craze for AI large models is just an illusory bubble.

Today, the situation is very different. The DeepSeek all-in-one machine is reasonably priced, and companies can at least use it quickly to conduct large model verification work in various sensitive scenarios internally. Regardless of whether the results given by the large model during the verification process are correct or not, the relevant problems will not be leaked to the external network, and will not cause adverse effects in society, which greatly reduces the psychological burden of enterprise innovation.

The DeepSeek all-in-one machine achieves vertical penetration from infrastructure to business scenarios by deeply integrating the GPU resource orchestration system, the enterprise-level model life cycle management platform and the intelligent application framework, allowing enterprises to focus on business scenario innovation rather than technology stack adaptation. To a certain extent, it can greatly reduce the threshold for enterprises to implement DeepSeek.

If some companies are unwilling to pay for fixed asset costs, can they first use the API service of DeepSeek on the domestic public cloud? After all, DeepSeek is completely open source, and the open source agreement is very friendly. Any cloud vendor can freely deploy and provide services to the outside world.

2. Currently, computing resources are in short supply, and even cloud vendors cannot guarantee services

When people used the standard service of Deep Seek's website at the beginning of the year, they often encountered a system busy prompt after one conversation, and they had to wait for several hours for the next conversation.

It was not until the beginning of this year that domestic Internet giants stepped up their pace in the layout of AI big models. Previously, their internal computing power was insufficient to meet their own business needs. At present, large companies not only need to use AI to comprehensively upgrade existing businesses, but also need to provide massive AI services to the external market. As a result, the strong demand for computing power from both inside and outside has caused the computing power gap to expand sharply, and its tension is self-evident.

"Tens of billions of yuan in orders! Tencent purchases a large number of NVIDIA H20 chips!"

In 2025, the demand for AI applications by Chinese companies may increase tenfold or even a hundredfold. In a market environment where the supply and demand of computing power are seriously unbalanced (supply is tight and demand is growing exponentially), computing power resources are becoming a strategic scarce asset. Although public cloud vendors are currently seizing the AI computing power market through low-price strategies, their business models face fundamental challenges: GPU clusters are significantly weaker than CPU architectures in virtualization efficiency and scheduling flexibility, making it difficult for traditional cloud computing to replicate its profit margins based on resource reuse in the field of AI computing power.

The cost structure analysis of GPU clusters shows that when the computing power demand of an enterprise reaches a certain scale, the comprehensive cost of DeepSeek all-in-one machine and calling public cloud API will not be substantially different, and local deployment can even reduce cost fluctuations. This cost curve feature makes it a more economical choice for medium and large enterprises to adopt a hybrid deployment strategy (core computing power localization + elastic demand cloud expansion) when planning the large-scale implementation of AI applications.

In addition, for large enterprises, internal budgets, cost accounting and allocation rules are particularly complex. If each department applies for API budgets on the cloud, the cost of the process may be much greater than the API fee. At this time, applying for DeepSeek all-in-one machine through a one-time budget can meet the free use of most internal departments this year. The remaining budget only has electricity costs and a clear expenditure budget, which is more in line with the operating mechanism of large enterprises.

Furthermore, different departments have different business applications, business scenarios, and data properties. Even if they do not choose foreign large-model API service providers such as OpenAI, but choose domestic public clouds, they still need to conduct cross-departmental communication and decision-making when deciding which data can be uploaded to the public cloud. For example, in financial companies, the data sensitivity of the risk control department and the marketing department is very different. Coordinating the data upload of the two departments often requires multiple cross-departmental meetings. The cost of preparing and holding each meeting, and the time delay cost caused by communication and coordination, may far exceed the investment cost of the all-in-one machine.

From the perspective of market evolution, the industry was mainly in the technical verification stage in the first half of the year, and most companies explored application scenarios through training, learning exchanges, POC (proof of concept), etc. With the completion of key scenario verification in the middle of the year, the market will enter a period of intensive computing power deployment, and the demand for AI computing power will explode from Q3. This expectation has led to the adoption of the "computing power pre-locking" strategy in computing power-intensive fields such as finance and intelligent manufacturing, and the construction of a deterministic computing power guarantee system by deploying DeepSeek all-in-one machines in advance.

In recent years, due to the continuous shortage of computing power supply, even large-capacity enterprise-level graphics cards such as A100 and A800 purchased a few years ago are still expensive in the current market. If there are no new products and new suppliers that are more competitive in the short term, purchasing a DeepSeek all-in-one machine at this time is not a "wealthy" decision.

Of course, for some small and medium-sized enterprises, using DeepSeek's API service on the public cloud is definitely a more flexible option.

3. The big model is more like hardware than software and less like SaaS

The current big models are more like a piece of hardware. For example, once the DeepSeek 671B big model is deployed, it is difficult to iterate and optimize it every day. The cost of fine-tuning the model is very high and ordinary companies cannot afford it. The big model is not the "software application" we imagined.

For locally deployed application software, some companies with good development capabilities can release a version every month or even every week. If enterprise users want to achieve more frequent iterations, they not only need to build a complex technical system covering the entire process of development, testing, and deployment, but also have to bear additional costs such as version compatibility, data migration, and internal high-availability cloud environment.

Compared with locally deployed software, SaaS and cloud services have shown significant advantages in version iteration efficiency. Through the automated support of the continuous integration/continuous deployment (CI/CD) pipeline, SaaS service providers can achieve weekly or even hourly grayscale iterations to ensure that functional updates evolve in sync with user needs. This rapid response capability relies on a centralized cloud management architecture, which not only avoids the cumbersome version distribution process of traditional local software, but also ensures the quality of updates through mechanisms such as AB testing.

However, the iteration and update of large models is not as fast as software. A single complete training takes 3-12 months (data cleaning/model training/multiple rounds of verification), which is far faster than the traditional software iteration rhythm. The efficiency of distributed training clusters decreases exponentially with scale, and the control of trillion-parameter gradient noise is still a technical bottleneck.

A better big model requires better data, more preparation time, and larger parameters. The acquisition, cleaning, and labeling of high-quality data is complicated and time-consuming, requiring data collection, model design, and repeated debugging. The larger the parameter scale, the higher the computational workload and hardware requirements. These factors combined make it difficult for big models to iterate as fast as software, and are quite similar to the iteration cycle of hardware such as mobile phones and computers.

4. The input and output bandwidth of large models is not high, so there is no essential difference between placing them in the cloud or on the cloud

Large models such as ChatGPT and DeepSeek rely on text for input and output, and interact with large models through prompts. The efficiency of this communication method is similar to that of human language communication, with a maximum of dozens of characters transmitted per second. Compared with computer data processing speed and network bandwidth, this communication bandwidth is really limited.

A key factor in our choice of SaaS or public cloud is the "last mile" cost of network bandwidth. In many regions, especially in enterprise scenarios, network bandwidth costs remain high. Taking China as an example, the cost of enterprise commercial bandwidth may be more than ten times higher than that of civilian use. Home users can enjoy exclusive gigabit bandwidth for a few hundred yuan per month; but in enterprises, it is difficult to guarantee gigabit bandwidth resources for each person. Once a large amount of data interaction occurs between the cloud and the cloud, it is very easy to form a network bottleneck. Therefore, for enterprises whose businesses have Internet attributes and need to interact frequently with the outside world, such as financial institutions' 2C-oriented marketing systems, if 90% of data interactions come from the Internet, deploying the system completely in the cloud may be a better interaction solution.

In contrast, the extremely low communication bandwidth of large models is negligible compared to the high bandwidth capabilities of computers and networks. This means that if enterprises mainly use large models to handle internal business, processes and data, they can deploy large model all-in-one machines locally, allowing them to interact securely with local systems and sensitive data, and then call Internet resources and data query functions when necessary. This approach is feasible. Regardless of the company's positioning of the application scenario of large models - whether it focuses on internal transaction processing or external business development, the deployment location of large models on or off the cloud usually does not become an architectural bottleneck. The real limiting factor lies in the throughput of the large model itself, which is only a few dozen characters per second.

Summary and Thoughts

In the software field, the SaaS model may be better than locally deployed software. However, in the AI big model, there is no essential difference between on-cloud and off-cloud.

The characteristics of large models determine that they are different from traditional software. They are more like hardware, not easy to modify, with long iteration cycles and high fine-tuning costs. In terms of network bandwidth, large models have low input and output bandwidth, and there is little difference between cloud-based and cloud-based deployment. For enterprises, factors such as data security, cost budget, and business scenarios need to be considered comprehensively. Large enterprises can use DeepSeek all-in-one machines to verify internal sensitive scenarios and reduce the psychological burden of innovation.