RTX A4000 graphics card and Dell OptiPlex 7020MT Plus: a golden combination for DeepSeek local deployment

Written by

Silas Grey

Updated on:July-03rd-2025

As a professional-grade graphics card, NVIDIA RTX A4000 is an ideal choice for mid-to-high-end deep learning tasks with its 16GB GDDR6 video memory, 6144 CUDA cores and third-generation Tensor Cores. Its single-slot design and 140W low power consumption feature balance performance and deployment flexibility, making it particularly suitable for scenarios that require long-term stable operation.

The core advantage of RTX A4000 graphics card as an inference graphics card

Hardware performance adaptability

‌Memory capacity and bandwidth‌: Equipped with ‌16GB GDDR6 memory‌, the memory bandwidth is up to ‌448GB/s‌13, which can support batch inference tasks of medium-sized models (such as those with tens of billions of parameters) and reduce frequent data exchanges caused by insufficient memory‌. Supports ‌ECC memory error correction‌ to improve data stability of long-term inference tasks‌.

‌Computing core and efficiency‌: Based on the ‌NVIDIA Ampere architecture‌, it integrates ‌6144 CUDA cores‌ and ‌192 third-generation Tensor Cores‌, optimizes matrix operations in deep learning reasoning, and improves reasoning efficiency by more than 30% compared to the previous generation architecture‌. It supports ‌FP16, TF32, and BF16 mixed precision computing‌, balancing computing speed and model accuracy requirements‌.

DeepSeek model adaptability

DeepSeek-7B: Runs smoothly at full precision (FP16), occupies about 16GB of video memory, and supports complex dialogues and basic generation tasks.

DeepSeek-13B: Through Q4_K_M quantization (video memory requirement is about 8GB), it can process 8K context dialogues and meet high-precision scenarios such as code generation.

DeepSeek-70B: requires the combination of multiple cards or hybrid quantization (such as Q4_K_M + 8-bit hybrid). A single card A4000 can support experimental reasoning, but the video memory allocation strategy needs to be optimized.

Dell OptiPlex 7020MT Plus: An all-around battleship for high-performance deployment

Core configuration highlights:

Processor: 14th generation i7-14700 (20 cores and 28 threads), 5.4GHz turbo frequency, can easily handle model loading and parallel computing.

Memory: 32GB DDR5, expandable to 128GB, ensuring high-speed reading and writing of large model parameters.

Storage: 512GB PCIe4.0 SSD + 2TB HDD, taking into account both system responsiveness and massive data storage.

Graphics card: RTX A4000-16GB independent graphics card, providing professional-grade AI acceleration capabilities.

Practical deployment: from laboratory to production environment

Recommended application scenarios:

AI development for small and medium-sized teams: Based on DeepSeek-7B/13B, private knowledge base question and answer and code-assisted generation are implemented, and a single card can meet the demand of an average of 1,000 calls per day.

Vertical field customization: The financial and medical industries achieve high-precision industry model localization through LoRA fine-tuning combined with Q6 quantization.

Hybrid deployment solution: Run the 7B model locally to process sensitive data, and call the 70B model in the cloud to complete complex analysis, taking into account both security and cost.

Why choose this combination?

The combination of RTX A4000 and OptiPlex 7020MT Plus achieves a triangular balance of "performance-expansion-stability" at a cost of RMB 14,000. In the competition for the "last mile" of AI implementation, this solution opens up new possibilities for the private deployment of large models for small and medium-sized teams with hard-core configuration and pragmatic strategies.