It is not recommended to deploy DeepSeek-R1:32B or smaller models locally. The following table recommends a suitable host:

Written by

Jasper Cole

Updated on:July-14th-2025

Since the beginning of this year, as the DeepSeek-R1 distillation model has reduced its computer requirements, many AI enthusiasts have tried to deploy local models on personal computers, hoping to achieve functions such as efficient writing, knowledge base management, or code generation. However, the author found in actual tests that models with parameters less than 32B in the DeepSeek series (such as 1.5B, 7B , etc.) performed mediocre after local deployment , and could only meet basic conversation needs, but were not very useful in complex tasks. This article analyzes the reasons based on the actual test results, and recommends that enthusiasts deploy at least a 32B model locally. Finally, a host with a total price of about 10,000 yuan is recommended at the end of the article .

Small model local deployment test: obvious capability limitations

I tested the DeepSeek-7B model on my personal PC and found the following issues:

Assisted writing: Unable to generate content according to the framework requirements, logical gaps often occur, long texts lack coherence, and require frequent manual corrections.

Knowledge base construction: The understanding of professional terms is shallow, multiple rounds of questions and answers easily confuse core concepts, and effective knowledge associations cannot be established. English words often appear in answering questions, which is confusing.

Code generation : Only simple function snippets can be output, and complex requirements (such as multi-threading and API calls) often result in syntax errors.

You can read this article to pour a basin of rational cold water in the name of small models: Reflection on the limitations caused by the actual measurement of DeepSeek-R1 7B

However, by retrieving relevant information, the DeepSeek-32B model is relatively stable in the above tasks, and its understanding of relevant instructions is significantly improved, proving that the number of model parameters is the key threshold that determines performance .

Why are models below 32B not worth deploying locally?

Knowledge capacity bottleneck: The parameter scale of a small model (e.g. 7B, about 7 billion parameters) can only store basic language rules and lacks the depth of professional domain knowledge. In contrast, the "memory capacity" of a 32B model (32 billion parameters) grows exponentially and can support more complex semantic understanding.

Reasoning ability ceiling: In the Transformer architecture, the number of attention heads is positively correlated with the number of parameters. Small models have difficulty handling long-distance dependencies, resulting in poor logic in generated content, while the 32B model can maintain longer contextual coherence.

Unbalanced hardware utilization: Taking RTX 3060 as an example, when running the 7B model , the GPU utilization is less than 30% , and the video memory occupies only 8GB , but the marginal cost of performance improvement is extremely high. After 4-bit quantization, the 32B model occupies about 14GB of video memory , which can make full use of hardware resources.

The 32B model can be deployed, and a 10,000 yuan high-cost configuration solution is recommended, but netizens commented that the 3090 is still slow to run

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4. Deployment Optimization Suggestions

Using the vLLM inference framework , the speed is 3-5 times faster than HuggingFace Transformers

Adopt AWQ 4bit quantization technology to reduce the memory usage of 32B model to 14GB

Set up a paged attention mechanism ( PagedAttention ) to break the limit on the length of a single conversation

Conclusion

For AI developers who pursue practical value , the 32B model is the turning point of cost-effectiveness for local deployment. Instead of wasting time debugging low-parameter models, it is better to choose a reasonable hardware configuration to give full play to the productivity potential of large models. As the cost of graphics card memory continues to decline, the threshold for individuals to deploy professional-level AI tools is accelerating.