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How to choose DeepSeek full blood version vs distilled version? Which all-in-one machine is more cost-effective

Written by

Jasper Cole

Updated on:July-15th-2025

When choosing DeepSeek , whether to choose the full version or the distilled version requires a comprehensive evaluation based on specific business needs, hardware resources, cost budget, and application scenarios. The following is a detailed comparison and suggestions:

1. Performance and accuracy

Full Blood Version

Parameter scale : Based on 671B parameters (such as R1/V3 models), it supports ultra-long context understanding, and its functions cover complex reasoning, code generation (LeetCode problem pass rate 92%), scientific research paper framework generation, etc.
Hardware requirements : Professional servers are required (such as dual H100 GPUs + 1TB memory or 8-card A100 cluster), suitable for enterprise-level deployment.
Application scenarios : Suitable for highly complex tasks such as autonomous driving, financial risk control, medical image analysis, industrial quality inspection, or scenarios that require processing tens of thousands of words of government documents or PB-level data.
Security : Supports local deployment, no need to transmit data externally, and meets the high security requirements of medical care, government affairs and other fields .
version

Distilled version

Parameter scale : 1.5B to 70B parameters, functions focus on basic tasks (such as Python scripting, literature abstract translation), and the performance is only 1/10 of the full-blooded version .
Hardware requirements : Can be run on a single RTX 3090 card or a home PC, and version 1.5B (such as the MNN framework) can also be deployed on mobile phones .
Application scenarios : Suitable for lightweight needs, such as personal learning assistants, content creation, customer service conversations, or low-cost AI integration for small and medium-sized enterprises .

Recommended configuration list for each series model

Full-blooded quantization version: Many manufacturers' AI cards only support INT8, FP16, FP32 and other formats. If FP16 is used, a single machine needs more than 1.4T video memory. Most domestic AI stand-alone machines do not have such a large video memory. In order for a single machine to run 671B deepseek, quantization is forced to be chosen. Quantization is to reduce the calculation accuracy to reduce the video memory usage and improve the throughput efficiency. Of course, any quantization comes at the cost of lowering the IQ.

To give an illustrative example, for FP8 we say that the calculation retains 7 digits after the decimal point, and for INT8 we say that the calculation retains 2 digits after the data point.

The calculation of FP8 is: 3.1415926*3.1415926 =9.8696040,

The calculation accuracy of IN8 is 3.14*3.14=9.86

We think these two results are approximately equivalent, but we will find that FP8 is more accurate. In large models, we roughly think that the higher the accuracy, the higher the IQ. So we roughly think that FP8 has a higher IQ.

2. Hardware resources and costs

Full blood version :

Hardware cost : High-performance GPU or dedicated AI chip is required, and the hardware cost is relatively high.
Deployment cost : The deployment and maintenance costs are high and require a professional technical team to manage.
Inference latency : The inference latency is low, which is suitable for scenarios that require fast response.

Distilled version :

Hardware cost : The hardware requirements are low and the hardware cost is low.
Deployment cost : The deployment and maintenance costs are low, which is suitable for small and medium-sized enterprises and scenarios with limited resources.
Inference latency : The inference latency is higher, but suitable for resource-constrained devices.

3. Application scenarios

Full blood version :

Applicable scenarios : Suitable for scenarios that require high precision and high performance, such as financial analysis, drug development, and complex natural language processing.
User groups : large enterprises, scientific research institutions and other users who have extremely high requirements for model performance.

Distilled version :

Applicable scenarios : Suitable for scenarios with limited resources, such as edge devices, mobile devices, real-time interactive applications, etc.
User groups : Small and medium-sized enterprises, users with limited resources, and scenarios that require fast deployment and low hardware costs.

4. Selection suggestions

Give priority to the full blood version :

If your business needs require extremely high model accuracy and you have sufficient hardware resources and budget, it is recommended to choose the full-blooded version. The full-blooded version can provide the highest performance and accuracy, and is suitable for complex tasks and scenarios with high precision requirements.

Complex enterprise-level tasks : need to process high-precision reasoning (such as medical diagnosis assistance, financial modeling), large-scale data analysis, or require local deployment to ensure data security.
Scientific research and development : scenarios involving code generation, scientific research paper framework design, etc. that require high-parameter model support.
Sufficient computing resources : Own professional GPU servers (such as A100/H100 clusters) and have sufficient budget

For example, Huawei's full-blooded Ultra version all-in-one machine is designed specifically for scientific research and high-end enterprise services. It supports high-performance inference of models with hundreds of billions of parameters and meets the high computing power requirements of financial analysis, drug development, etc.

Choose distilled version :

If your business needs have relatively low requirements for model accuracy and are more sensitive to hardware resources and costs, it is recommended to choose the distilled version. The distilled version can significantly reduce hardware costs and deployment difficulty while maintaining high performance.

Lightweight applications : such as personal learning, basic programming, daily Q&A, or mobile scenarios with high requirements for response speed.
Limited resources : Small and medium-sized enterprises that are only equipped with low- or mid-end GPUs (such as RTX 3090) or need to control costs.
Rapid deployment requirements : hope to quickly integrate through API or use cloud services (such as Qiniu Cloud, Volcano Ark) to reduce the complexity of operation and maintenance

For example, Huawei's Distillation Pro all -in-one machine is designed for enterprise knowledge base question and answer and intelligent content creation scenarios. It supports dual engines of model fine-tuning and reasoning, and can quickly customize marketing copy generation, customer service assistant and other applications.

Considerations for selecting large model all-in-one machine

1. Domestic and trusted innovation: Domestic means produced in mainland China, which means that except for brands like HP and Dell, all other brands are called domestic; trusted innovation is divided into fully trusted innovation and semi-trusted innovation. Fully trusted innovation means that both the CPU and AI card are newly created, while semi-trusted innovation means that only the AI card part is trusted innovation, not the CPU.

2. Demand: Is it for trying out something new or just for show? In this case, the cheaper the better, and experience is the priority; if it is for business use, you need to sort out in advance whether the business is suitable for the big model?

3. Concurrency: Generally, the number of people in the company/20 is the required concurrency formula. You can be online at the same time, but the number of concurrent users cannot be too high.

4. Security: The most important thing about big models is their security. There is no good technical strategy at present. The best thing is to deploy a big model all-in-one machine in each department, and access different models from each other. For example, the finance department, legal department, contract department, etc. are separated. For example, if someone asks how much Zhang San's salary is, the big model will accurately query the HR database and give a precise answer.

5. Cost: If you have enough money, you will definitely choose the original full-blood version, followed by the quantitative full-blood version, and finally the distilled version. Currently, the cheapest quantitative full-blood version is 98,000 yuan, and the most expensive original full-blood version H200 is more than 2 million yuan.

6. Implementation: Which product do you buy to experience? Out of the box or do you have your own technicians to tinker with it? Deepseek will definitely be integrated with ERP, CRM, OA, etc. in the enterprise to reduce a lot of people's workload.

7. Operation: There are three ways to run the 671B large model: video memory operation, internal memory operation, and hard disk operation. The tokens/S speeds and prices of the three methods are different. You can choose the one that suits you.

5. Deployment and usage recommendations

Full blood version :

Huawei FusionCube A3000 training and promotion hyper-converged all-in-one machine : supports the full version of DeepSeek , is designed for scientific research and high-end enterprise services, and supports high-performance inference of models with hundreds of billions of parameters.
Baidu Baige DeepSeek All-in-One : Supports the deployment of 8 cards on a single Kunlun Core P800 machine , provides a purely domestic computing power combination, supports 8-bit reasoning, and provides computing power scheduling management, model training acceleration, visual operation and maintenance monitoring and other capabilities.

Distilled version :

Huawei FusionCube A3000 Distillation Pro Edition : Aimed at enterprise knowledge base question and answer and intelligent content creation scenarios, it supports model fine-tuning and reasoning dual engines, and can quickly customize marketing copy generation, customer service assistant and other applications.
Baidu Qianfan DeepSeek all-in-one machine : pre-installed DeepSeek distillation and fine-tuning tool chain, supports full-blood model distillation, and provides a variety of distilled models, such as DeepSeek-R1-Distill-Qwen-32B , DeepSeek-R1-Distill-Qwen-14B , etc.

Hybrid deployment solution : If the scenarios are diverse, you can combine the advantages of both. For example, the core business uses the full-blooded version to handle complex tasks, and the edge device deploys the distilled version to respond to real-time requests .

Trial evaluation : Experience the full-blooded version of the API for free through third-party platforms (such as Silicon Flow and Volcano Ark), or use tools such as Ollama to test the local performance of the distilled version before deciding on a purchasing strategy .
Focus on ecosystem support : The full-blooded version is usually equipped with enterprise-level services (such as Ningchang and Capital Online's all-in-one solutions), while the distilled version is more suitable for developers to adapt independently

Summarize

V1: Suitable for programming and text processing, simple and easy to use.

V2/V2.5: High cost-effectiveness, suitable for general scenarios with limited budget.

V3: Fast speed, multi-language support, suitable for a wide range of knowledge quizzes and creations.

R1: Focuses on mathematics and code, suitable for professional developers.

671B full-blooded version : It has top performance but requires powerful hardware support. It is suitable for scenarios with extremely high requirements for model accuracy, such as financial analysis and drug development. It requires high-performance hardware and higher deployment costs.

Distilled version : Suitable for resource-constrained scenarios, such as edge devices, mobile devices, and real-time interactive applications, with lower hardware costs and deployment difficulty.

According to the parameter scale, the independent deployment configuration requirements are summarized as follows:

1.5B-8B: Suitable for individual developers or small teams, with low cost and low hardware requirements .

14B-32B: Suitable for medium-sized enterprises or research institutions that require higher-configuration graphics cards and memory.

70B-671B: Suitable for large enterprises or ultra-large-scale tasks, with extremely high hardware and cost requirements, and usually used for distributed training.

Choose according to your needs, don't pay for "high configuration"! According to specific needs and resource conditions, choosing the appropriate version can better meet business needs while optimizing costs and performance.