What hardware configuration do you need to prepare for private deployment of DeepSeek?

Recently, as the domestic large model technology continues to ferment,  a series of models released by DeepSeek have attracted widespread attention due to their high efficiency , low cost , open source and other characteristics. According to information released by multiple evaluation agencies, the performance of the R1 model released by DeepSeek has approached or even surpassed the GPT-o1 developed by OpenAI in the United States , and has become an important player in the field of AI.

However, considering data privacy and security issues, many companies want to deploy their own private DeepSeek model service locally. A key challenge is how to choose the appropriate hardware configuration?

Below, I will provide you with a detailed introduction to the hardware configuration required for private deployment of DeepSeek's latest R1 series models for your reference.

  ① CPU  

The CPU is the core brain of the computer . Although the GPU plays a dominant role in AI computing, the role of the CPU cannot be ignored. It is responsible for coordinating the work of various components of the system and performing basic data processing and scheduling.

If you want large models to run smoothly, the minimum requirement is Intel Core i7 or AMD Ryzen 7 (multi-core processor) series. Recommended: Intel Xeon  or  AMD EPYC series , main frequency above 2.5GHz, 32 cores or more. Only such high-performance CPU can better cooperate with GPU to complete the reasoning process of large models.

  ② Running memory  

Memory size is directly related to the amount of data that DeepSeek can load and process when running  . Recommended configuration: 32GB RAM  or more. For larger models (such as 70B and above), 64GB or more is recommended to reduce data reading time and improve operation efficiency. Insufficient memory will also cause the system to run slowly, seriously affecting the user experience.

  ③ GPU (commonly known as graphics card)

The GPU is the core of DeepSeek's local deployment. It is responsible for performing a large number of computing tasks. Functions such as model fine-tuning, knowledge base document vectorization, and model reasoning all rely on the GPU. Therefore, the performance of the GPU also directly affects the running speed and efficiency of these work links.

For models with parameter sizes of 32B, 70B, or more, you need to have a GPU with at least 24GB of video memory, such as NVIDIA's A100 , H100 , and other high-end GPUs. These GPUs have powerful parallel computing capabilities and can quickly process complex model inference operations, ensuring that DeepSeek runs smoothly and efficiently.

  ④ Hard disk storage  

This mainly depends on the size of the company's existing corpus data. At least 500GB or more of solid-state drive  SSD  should be prepared . Because SSD has fast reading and writing speeds, it can greatly shorten data access time and improve model loading and training speed.

High read and write speeds can ensure fast data transmission. The hard disk is the relatively cheapest component in the hardware. As long as the cost allows, try to make the space as redundant as possible to avoid it becoming a performance bottleneck for running large models.

  ⑤ Heat dissipation and electromagnetic shielding  

Large model training and reasoning usually require hardware devices such as high-performance GPUs, which consume high power and generate a lot of heat . Therefore, a good heat dissipation system is the key to ensuring stable operation of the hardware. High-performance radiators, such as water-cooled radiators , can effectively reduce the temperature of the hardware.

In addition, high-performance hardware may generate electromagnetic interference and affect other devices. High-quality electromagnetic shielding measures can reduce interference between hardware and ensure the stability and reliability of large model systems.

  ⑥ Network and security equipment  

If you need to obtain data from the network or communicate with other devices , stable network equipment is also essential. It is recommended to equip at least a Gigabit network card to meet basic network requirements. If the deployment environment involves multiple servers or devices, it is recommended to use a switch that supports PoE (Power over Ethernet) to facilitate device deployment and management.

At the same time, network security is also particularly important in private deployment . It is recommended to configure a strict firewall and enable security groups to limit unnecessary port access and prevent external attacks . If the service needs to support remote access, it can also be combined with VPN, reverse proxy and other technologies to achieve secure external network access. And equipped with some identity authentication, auditing and traffic monitoring  technologies for security protection.

  ⑦ Detailed hardware configuration reference  

The following are the detailed minimum hardware configuration requirements corresponding to each parameter model of DeepSeek R1 for your reference. Please note that it is the minimum. The specific hardware that can smoothly run these models should be reasonably evaluated based on  factors such as the size of your dataset and user access volume  .

DeepSeek-R1-1.5B : Minimum 4-core CPU, 8GB+ memory, 16GB+ storage, graphics card optional.

DeepSeek-R1-7B : 8-core+CPU, 16GB+memory, 32GB+storage, recommended graphics card with 16~24GB video memory.

DeepSeek-R1-8B : slightly higher than 7B configuration.

DeepSeek-R1-14B : 12 ​​cores + CPU, 32GB + memory, 64GB + storage, 24GB video memory graphics card is recommended.

DeepSeek-R1-32B : CPU with 16 cores or above, 64GB+ memory, 128GB+ storage, and a graphics card with 48GB video memory is recommended.

DeepSeek-R1-70B : CPU with 32 cores or above, 128GB+ memory, 1TB+ storage, and a graphics card with 96GB video memory is recommended.

In short, if you really want to buy your own hardware for local deployment, you must choose the hardware equipment reasonably according to your own needs and budget, so that DeepSeek can perform best locally.

The lecture representative advised

If you currently want to make some preliminary attempts to apply large model technology to verify its feasibility and effectiveness , I personally do not recommend that you invest a large amount of money to purchase hardware for environment deployment. You can try to use the official open API capabilities, or use the full-blooded version of Deepseek-R1 model service built by major cloud providers (Alibaba Cloud, Huawei Cloud, Tencent Cloud) for initial trial.

Deploy your own DeepSeek model in the cloud. It not only has low deployment threshold and cost , supports various model sizes , but also can be automatically  elastically expanded . Isn’t it great?

After all, the technology of big models is still developing and changing rapidly with each passing day . You can consider applying the capabilities of big models at a lower cost first, evaluate  the effect of their application and the value they generate  , and then consider further private deployment plans.