Comparison of private deployment costs of different versions of DeepSeek: How can enterprises choose the best solution?

Written by

Iris Vance

Updated on:July-15th-2025

In 2025, with the explosive growth of DeepSeek open source models, the demand for private deployment of AI by enterprises has shown a polarized trend. On the one hand, models such as R1 and V3, with the label of "performance benchmarking GPT-4, cost only 10%", promote AI from the laboratory to the core scenarios of the industry; on the other hand, hardware investment of millions of dollars and the complexity of computing resource allocation have also put enterprises in the dilemma of "efficiency and cost". This article will dismantle the private deployment solutions of different versions of DeepSeek from the dimensions of hardware configuration, bandwidth requirements, comprehensive cost, etc., to provide enterprises with a feasible decision-making framework.

1. Overview of DeepSeek Core Version and Hardware Requirements

The version iteration of DeepSeek follows the technical route of "performance improvement and cost reduction in parallel". From V2 in 2024 to R1 in 2025, the model parameters jumped from 67 billion to 671 billion, but through the hybrid expert architecture (MoE) and algorithm optimization, the training cost was reduced to 1/100 of similar models. The following are the key features of the mainstream deployment version:

Please note that the above configuration is the minimum requirement, and actual deployment may need to be adjusted according to specific application scenarios and performance requirements. In addition, deploying high-parameter models (such as 70B and above) requires high-performance hardware, which may be difficult for ordinary personal devices to meet. It is recommended to consider using cloud services or professional computing clusters.

2. Hardware cost: the difference in investment from "lightweight" to "full-capacity version"

"Server busy, please try again later" is a common problem encountered by DeepSeek users recently. The surge in users has kept DeepSeek running at full computing capacity. Therefore, many individual users and enterprises have begun to turn their attention to "privatized deployment".

The hardware cost of enterprise private deployment mainly depends on the model scale and computing power carrier selection. Deploying high-parameter models (such as 70B and 671B) usually requires multiple nodes to work together. The overall investment not only includes the hardware purchase cost, but also involves the cost of computer room construction, heat dissipation, electricity, and operation and maintenance management. It is difficult for us to calculate this part of the cost. If only Koala hardware is used, the hardware cost of self-built clusters is estimated as follows:

In addition, it should be noted that currently high-end NVIDIA graphics cards are difficult to buy in China. Most companies purchase them overseas through Hong Kong or Singapore companies, so the price of graphics cards fluctuates greatly. In addition, self-built clusters also need to consider subsequent maintenance costs and labor costs, which are the "cost pits" hidden behind. Companies that build their own clusters must reserve sufficient funds to deal with them.

However, if you use cloud services instead of self-built clusters, you can save a lot of costs, such as DigitalOcean's H100x8-based GPU Droplet and bare metal servers, or the latest DigitalOcean H200 bare metal server.

Additional benefits that DigitalOcean services offer :

The GPU Droplet bare metal model ensures data privacy through physical isolation and is suitable for high-security scenarios such as Web3 and finance.
GPU Droplets are provided with security measures (such as network isolation and firewalls) by cloud service providers, but users need to manage application layer security themselves.
GPU Droplet supports one-click deployment of large models such as DeepSeek.

3. Bandwidth cost: the hidden cost under concurrent pressure

The reliance of model reasoning on network bandwidth is often underestimated. Different versions of DeepSeek require different bandwidth resources when providing reasoning services. This needs to be considered whether you are building your own cluster or using a cloud service cluster.

70B version : The model parameters are relatively small, and the input, output, and some intermediate activation data are mainly transmitted during inference. Its peak bandwidth requirement can be regarded as a "baseline" level (the specific number varies depending on the deployment environment and optimization strategy).
Version 671B : The number of model parameters is about 10 times that of the 70B version. Not only do more parameters need to be loaded during inference, but the amount of intermediate activation data will also increase significantly. This means that under the same request concurrency and response speed requirements, the amount of data transmission will increase significantly, and the peak bandwidth may need to be about 5 to 10 times higher than the 70B version, or even more.

It should be noted that the actual bandwidth requirement is also affected by many factors such as parallel strategy, model compression, pipeline scheduling, etc. But in general, if the 671B version is used to process inference tasks, a higher bandwidth guarantee must be provided for the network, otherwise it will easily become a bottleneck for overall performance.

Now that we have considered bandwidth, we have to talk about traffic costs. Because when your service encounters high concurrency, your traffic may account for a large proportion of your bill. Let's take cloud services as an example:

summary

Although private deployment can ensure data security, it is recommended to give priority to cloud services in the following situations:

Short-term needs or test projects : For example, startups can consider Spot instances. Although the service will not be very stable, the cost is low.
Significant fluctuations in computing power : During e-commerce promotions, sudden traffic bursts can be elastically expanded through cloud services, with costs 40% lower than self-built clusters.
Technology iteration risk : The DeepSeek model is updated once a quarter on average, and the cloud service can automatically synchronize the latest version to prevent the local model from being "locked in the old architecture."

The explosion of DeepSeek is not only a technological revolution, but also a "computing power equality movement". Enterprises need to find a dynamic balance between data sovereignty, cost efficiency, and technological iteration. When AI becomes the "new oxygen", the essence of choosing a deployment strategy is to bet on core competitiveness in the next decade.

Unlike other cloud service providers, DigitalOcean H100/H200 bare metal servers provide:

Transparent pricing: no hidden fees, no complicated billing models, affordable prices
Fast deployment: Equipment delivery cycle only takes 1-2 working days
Full stack support: complete technical support from infrastructure to application layer
One-click model deployment: Supports one-click deployment of mainstream models such as DeepSeek and Llama