Learn about DeepSeek's private deployment costs in one article: how do organizations choose?

In today's era of rapidly evolving artificial intelligenceDeepSeekAs a leading AI model, its powerful functions and wide range of application areas make it the first choice of many enterprises. On the one hand, R1, V3 and other versions of the model by virtue of the "performance benchmark GPT-4, the cost of only 10%" label, to promote AI from the laboratory to the industrial core scene; on the other hand, the hardware investment can often be millions of dollars, the arithmetic resource allocation complexity and other issues, but also let the enterprise is caught in the" efficiency and cost" trade-off dilemma. Efficiency and cost" trade-off predicament.

In some areas, local deployment is necessary for organizations to ensure data privacy and security, avoiding the risks that may be associated with uploading data to third-party servers; it can provide faster response times and reduce network latency, especially in scenarios where sensitive data is processed or real-time response is required. In addition, local deployment supports more customization features, such as fine-tuning models to fit specific tasks, to meet the individual needs of enterprises.

In this article, we will dismantle DeepSeek's different hardware configurations, bandwidth requirements, and comprehensive costs in terms of dimensions such asPrivate deploymentPrograms that provide businesses with a framework for groundable decision-making.

I. Overview of DeepSeek core versions and hardware requirements

DeepSeek's version iterations have followed the technology path of "performance improvement and cost compression in parallel". From V2 in 2024 to R1 in 2025, the model parameters jumped from 67 billion to 671 billion, but the cost of training was reduced to 1/100th of the cost of comparable models through MoE and algorithmic optimizations.Below are the key features of the mainstream deployed versions:

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

II. Self-build program

2.1 Hardware costs

The hardware cost of enterprise private deployment mainly depends on the model size and the choice of arithmetic carrier. Deploying high-parameter models (e.g., 70B and 671B) usually requires multiple nodes to work together, and the overall investment not only includes hardware acquisition costs, but also involves the costs of server room construction, heat dissipation, electricity, and operation and maintenance management, which are difficult to calculate. In the case of Koala hardware alone, theHardware costs for self-built clustersThe prognosis is as follows:

In addition, it should also be noted that the current high-end NVIDIA graphics cards in the country are very difficult to buy, most enterprises will be through the Hong Kong or Singapore companies to purchase overseas, so the price of graphics cards fluctuate greatly. Moreover, self-built clusters also need to consider the subsequent maintenance costs and labor costs, which are hidden behind the "cost pit", self-built clusters of enterprises to leave sufficient funds to deal with.

2.2 Bandwidth costs

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

Low concurrency scenarios (<100 people): The R1-32B model can control the response latency within 200ms at 10Gbps bandwidth, with an annual bandwidth cost of about $120,000.
Highly concurrent scenarios (>1000 people): R1 full-blooded version requires 40Gbps dedicated channel, latency needs to be compressed to less than 50ms, annual bandwidth cost soared to 1.8 million dollars.

2.3 Operation and maintenance costs

1) Deployment complexity: a must for specialized teams

Local deployment needs to complete the environment configuration, model optimization, parameter debugging (such as temperature value 0.5-0.7, context overflow processing) and other steps. Ordinary users who lack experience in Linux system operation and maintenance and Docker containerization deployment are very likely to fail due to compatibility issues.

2) Hidden expenses of long-term operation and maintenance

Electricity consumption: High-performance servers can consume up to 3-5 kWh of electricity per hour, with annual electricity costs exceeding 100,000 RMB.
Technology Iteration: Model updates require redeployment, hardware needs to be upgraded every 3-5 years, and the average annual cost increase is about 151 TP4T.

2.4 Comprehensive comparison

In terms of private deployment scenarios only, different versions of the model require different hardware and bandwidth costs. If we calculate based on the data currently available on the web, the corresponding cost and ROI cycle for different versions are roughly as follows:

Version Type	Hardware costs ($ million)	Annual bandwidth cost ($ million)	Applicable enterprise size	ROI recovery cycle (years)
R1-32B	30-50	12-25	Small and medium-sized enterprises (<500 persons)	1.5-2
R1-70B	200-300	40-80	Medium-sized enterprises (500-2000 persons)	2-3
R1 Full Blooded Edition	600-2000	120-180	Large groups/financial institutions	3-5
V3 Distilled	30-50	8-15	Edge Computing Scenarios	<1

 

III. All-in-one solutions

Because the cost of local deployment remains high and the bar is high, many vendors have introduced all-in-one solutions.

The All-in-One PC integrates all required hardware and software, realizing rapid deployment and out-of-the-box use, which significantly shortens the system on-line time; the high degree of integration of hardware and software ensures the stability and efficient operation of the system, which reduces the complexity and cost of the later operation and maintenance; and the localized deployment effectively guarantees the security and controllability of the data and avoids the security risks that may be brought about by the cloud storage and transmission.

In addition, the All-in-One solution provides flexible resource allocation and scalability, which can be customized according to the actual needs of the enterprise to meet diverse application scenarios, and can bring an efficient, stable, secure and flexible deployment experience for the enterprise.

We focus onLongxin, Jingdong Cloud, Lenovo, Baidu Intelligent Cloud, Huawei AscendantDeepSeek all-in-one from five major manufacturers, from theTypical Configuration, Reference Price, Applicable ScenariosThree dimensions of in-depth analysis to help you accurately match your needs.

3.1 Longxin DeepSeek Reasoning All-in-One: The Choice for Government and Financial Security

Typical Configuration

• software: Longxin 3C5000 processor x 2 + Acer T100 accelerator card x 4 + 512GB DDR5 RAM + 8TB NVMe SSD
• hardware: DeepSeek Full Series Models (7B-70B) + Galaxy Kirin V20 OS + State Secret Algorithm Encryption Module
• arithmetic power: Support 100 people concurrent reasoning, single card FP16 arithmetic up to 128 TFLOPS

reference price

• basic version(7B model/2 accelerator card):$498,000
• flagship edition(70B model/4 accelerator card):1.18 million dollars.
• Including 3 years of maintenance services, government procurement can enjoy a maximum of 30% special subsidies

Suggestions for selection

Recommended Scene:Government agencies, financial institutions, military industrial units and other areas with high data security requirements.
A guide to avoiding the pit:If you only need a small model under 7B, you can reduce the number of acceleration cards to lower the cost; it is recommended to choose the Unisys UOS system to be compatible with more domestic software ecosystems.

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3.2 Jingdong Cloud vGPU Intelligent Computing All-in-One: The King of Price/Performance for SMBs

Typical Configuration

• software: Intel Xeon 6338N processor x 2 + China GPU (NVIDIA A10 performance equivalent) x 4 + 384GB RAM + 16TB storage
• hardwareDeepSeek V3/R1 model + self-research computing power pooling engine + intelligent operation and maintenance management platform.
• arithmetic power: Single machine supports 500 people concurrently, and the inference speed is improved by 50% compared with the open source solution.

reference price

• Standard Edition(with 2 domestic GPUs):$286,000(First year of free operation and maintenance)
• Premium Edition(with 4 domestic GPUs):$452,000(Free 3 months of cloud service resources)
• Supports monthly rental (minimum)RMB 9800/month)

Suggestions for selection

Recommended Scenarios: Lightweight scenarios such as e-commerce customer service, educational institutions, and office automation for SMEs.
Guide to avoiding the pitIf you need to run a large model above 70B, it is recommended that you pair it with Jingdong Cloud's public cloud computing power expansion; prioritize hybrid cloud deployment to balance cost and elasticity requirements.

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3.3 Lenovo DeepSeek Training and Push All-in-One: A 100 Billion Model Training Tool

Typical Configuration

• training versionLenovo SR850 server x 2 + Muxi C500 GPU x 8 + 1TB RAM + 50TB all-flash storage
• Reasoning Edition: SR670 server × 1 + Muxi C300 GPU × 4 + 256GB RAM + 10TB storage
• hardware: AI Force Intelligent Body Development Platform + DeepSeek-200B pre-trained models

reference price

• All-in-one training machine::2.38 million dollars(with 100 billion parameter model tuning services)
• Reasoning All-in-One::760,000 dollars.(with 100 hours of technical on-site support)

Suggestions for selection

Recommended Scenarios: High-end research fields such as university laboratories, automotive companies' autonomous driving research and development, and pharmaceutical companies' molecular simulation.
Guide to avoiding the pit: The training version needs to be supported by a liquid-cooled machine room (additional about $200,000); it is recommended that the vendor be required to provide technical support for model compression when purchasing in order to reduce inference costs.

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3.4 Baidu Intelligent Cloud Kunlun Core All-in-One: Benchmark for Private Deployment

Typical Configuration

• softwareKunlun Core P800×8 + AMD EPYC 9654 Processor×2 + 768GB RAM + 20TB SSD
• hardware: DeepSeek R1 Enterprise Edition + Chifan AI Development Platform Localization Kit
• arithmetic power: Standalone throughput 2437 tokens/s, latency <50ms

reference price

• Standard Edition::820,000 dollars.(including 3 years of 7 x 24 remote operation and maintenance)
• Promotions: Sign up and get 150,000 RMB worth of Chifan platform API call credits!

Suggestions for selection

Recommended Scenarios: Internet companies, retail chains, smart healthcare, and other industries that require rapid iteration of AI applications.
Guide to avoiding the pitIf there are obvious peaks and valleys in the business, it is recommended to pair with Baidu public cloud to achieve elastic expansion and contraction; note that Kunlun Core P800 does not support FP8 quantization for the time being, and model accuracy needs to be optimized in advance.

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3.5 Huawei Rise Atlas DeepSeek All-in-One: Full Scene Coverage Specialist

Typical Configuration

• software: Rise 910B chip × 8 + Kunpeng 920 processor × 2 + 1TB RAM + 30TB distributed storage
• hardware: MindSpore framework + DeepSeek-70B industry fine-tuned model library
• arithmetic powerSupport 2000 people concurrently, support training and reasoning mixed loads

reference price

• basic version::1.56 million dollars.(including 20 industry model fine-tuning services)
• Corporate Packages::2.99 million dollars.(Including 5 devices + Rise Cloud Management Platform)

Suggestions for selection

Recommended Scenarios: Energy, transportation, manufacturing and other complex industries that require multi-scenario AI collaboration.
Guide to avoiding the pitIt is recommended to choose Huawei's "Star Flash" network upgrade package (+120,000 RMB) to enhance the efficiency of multi-computer interconnection; and prioritize the procurement of MFPs with pre-installed industry models to shorten the deployment cycle.

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3.6 Selection Decision Tree: Three Steps to Targeting the Best Solution

Clear prioritization of requirements

• Safety first →Loongson (a family of general-purpose CPUs developed within China)
• Limited budget →Jingdong Yun (Beijing 2008-), China's largest cloud provider
• Kilobillion Training →associate (cognitively)
• Fast on-line →Baidu
• Multi-scene integration →Huawei (brand)

Arithmetic cost measurement formula

Single Reasoning Cost= (total equipment cost ÷ depreciation + annual O&M) ÷ annual estimated number of inference calls
Example: The annual inference cost of the Jingdong Cloud Premium Edition is about $0.003/time, which is significantly lower than the public cloud API ($0.01/time)

Hidden cost warning

• Domestic GPU eco-adaptation may increase 10%-30% development costs
• 15%-20% of server room modification budget needs to be set aside for private deployment
• Electricity costs need to be considered for large model training (~$0.8/kWh)

IV. Summary

For the enterprises themselves, there is a shift from "can't afford to buy, can't afford to use, can't afford to keep" to "on-demand purchasing, quick results, continuous evolution". Enterprises should always stay awake in this AI wave, make the best use of their resources and choose the most suitable solution for them.

At present, the DeepSeek all-in-one PC market has formed three camps, namely "security", "cost-effective" and "high-end training". Enterprises need to closely follow the business pain points in selecting the model: government and finance Priority localization rate, small and medium-sized enterprises focus on TCO (total cost of ownership), scientific research institutions focus on arithmetic density. It is recommended to apply for the vendor's test machine (usually free for 3-7 days) before purchasing, to test the throughput and latency performance in business scenarios. With the continued breakthrough in the performance of domestic chips, the second half of 2025 may usher in a new round of price wars, non-urgent demand can be appropriate wait and see.

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