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DeepSeek 32B runs freely, assembling AI large model computer host at a great value of 10,000 yuan

Written by

Clara Bennett

Updated on:June-27th-2025

The best way to use AI big models is to visit the official website or official application interface (API) online, which has the best effect and the lowest cost. After all, the official uses the highest-end hardware, full-blooded version, and the highest intensity training. However, as a hobbyist, you like to tinker and often download the big model to the local computer for testing. At this time, it is difficult for ordinary computers to run effectively. After all, most ordinary computers are not equipped with independent GPU graphics cards, or the graphics card is less than 2G, and concurrent computing cannot be satisfied. The large model host has extremely high requirements for GPU graphics cards.

So, how to assemble a cost-effective AI large model computer host?

In order to meet the calls of some students, we have assembled a super-value configuration with a budget of tens of thousands of yuan to serve as a starting point for discussion.

Let's first introduce the logic of assembling the AI computer host. The core components of the AI host are five major parts: motherboard, CPU (processor), memory, GPU (graphics card) and hard disk, which account for 90% of the budget expenditure. In the AI host, the focus is on launching suitable configurations from demand, GPU, CPU, memory, hard disk, motherboard, and selecting suitable module components. Our demand is to smoothly run the DeepSeek-R1-Distill-Qwen-32B large model with 32B parameters, and at the same time be backward compatible with large models such as DeepSeek-R1-Distill-Qwen-14B, Qwen2.5-7B, and take into account applications such as games, image generation, and audio generation.

First the configuration, then the item by item description.

Components

Model / Specification

Require

Price (Yuan)

Remark

CPU

AMD Ryzen 5 5600X

6 cores , 12 threads, 3.7-4.6GHz

650

Basic computing support

GPU

NVIDIA RTX 3090

24GB GDDR6X , 10496 CUDA cores

6500-9000

Video memory meets 4-bit model requirements

Motherboard

MSI B550M PRO-VDH

PCIe 4.0 x16

600

Good compatibility and high cost performance

Memory

32GB DDR4 3200MHz

2 x 16GB

500

Satisfy model loading and context processing

storage

Western Digital SN770 1TB NVMe

1TB , 5150MB/s read

500

Storage model files (about 40GB )

power supply

Great Wall GX650 650W

80PLUS Gold

500

350W power consumption of 3090

Chassis

Jonsbo C6

ATX , supports large graphics cards

200

Compact design, good heat dissipation

Heat dissipation

Cooler Master T400i

Tower air cooling

100

Keep your CPU stable

Total Price

9050-12500 Yuan

1. GPU

Since DeepSeek-R1-Distill-Qwen-32B needs to be run, 21GB of video memory is required after 4-bit quantization. Then a single NVIDIA RTX 3090 (24GB video memory) can meet this requirement. Students who have the conditions can choose NVIDIA RTX 4090 (24GB video memory), which improves performance by 1.5-3 times, and takes into account the gaming experience (supports DLSS 3 frame generation), and has better image support.

Of course, there are differences between GPUs such as A cards (AMD) and N cards (NVIDIA). Here we recommend using mainstream N cards, mainly because the N card ecosystem is mature, many large models are based on CUDA training of N cards, and they have better compatibility, and Tensor Core accelerates matrix calculation and reasoning speed (such as Transformer's attention mechanism reasoning), making them more cost-effective.

2. CPU

After weighing the needs and budget, the CPU of choice is AMD Ryzen 5 5600X, and AMD Ryzen 9 5900X is recommended.

The 5600X has 6 cores and 12 threads, a base frequency of 3.7G, and an L3 cache of 32M, which is sufficient for running and reasoning small and medium-sized models, and the price is just over 650 yuan. The 5900X has 12 cores and 24 threads, double the parallel capability, and plays a greater role in long context processing and multi-tasking loading, but the price is 2,500 yuan, which is much more expensive.

Because the CPU is mainly responsible for model loading, data preprocessing and a small amount of calculation offloading, the requirements are not that high, and the budget can be left for the GPU, so the 5600X basically meets the requirements. As a note, because AMD CPUs are more cost-effective and have better multi-threading support, Intel CPUs of the same level should be abandoned under tight budget conditions.

Memory

According to demand estimates, the maximum memory usage is expected to be 32GB , of which the 4-bit quantized model file is about 16GB in size and temporarily occupies RAM when loading, which is usually 1.5-2 times larger than the file (about 24-32GB) .

Therefore, the minimum memory configuration is 32GB DDR4 3200MHz and can handle short-length contexts.

64GB DDR4 3600MHz is recommended here to support long context and improve loading speed of large models.

It is recommended to install two memory sticks into the motherboard slots, mainly to make full use of the dual-slot dual-channel and improve concurrent performance.

4. Hard disk

It is recommended to use Western Digital SN770 1TB. The hard drive has sufficient performance, with interfaces of PCIe 4.0 x4 and NVMe, sequential read/write speed (IOPS) of 5150/4900 MB/s, random read/fetch speed (IOPS) of 650K/800K, and meets short context reasoning (4K tokens). As we all know, the sequential read and write speed of mechanical hard drives is capped at 300 MB/s, and NVMe hard drives are nearly 20 times faster.

If the budget is sufficient, the Samsung 990 PRO 2TB hard drive is recommended. The interface is also PCIe 4.0 x4, NVMe, providing double the space, sequential read/write speed is about 44% faster than SN770, and random read/fetch speed is about 41% faster. It can accommodate models, data sets and more files in the future, suitable for long-term use. At the same time, the hard drive has 2GB DRAM cache, maintaining stable high performance, suitable for AI tasks.

5. Motherboard

The MSI B550-A PRO motherboard is recommended here, mainly because it is cost-effective and costs only 600 yuan.

The B550 chipset of this motherboard matches the high bandwidth requirements of the RTX 3090; it provides 1 PCIe 4.0 x16 slot for the GPU to ensure GPU data transmission efficiency; it provides 2 M.2 slots (1 PCIe 4.0, 1 PCIe 3.0) for M.2 NVMe SSD hard drives; and it provides 4 DDR4 memory slots (up to 128GB, 4400MHz OC).

The motherboard also has lower power consumption, does not require a chipset fan, and runs more quietly.

There are disadvantages, the main one is that the expandability is not high. If you need to expand more GPUs and have sufficient budget, then the ASUS TUF Gaming X570-Plus motherboard is recommended.

6. Power supply

Configure the power supply based on the GPU power consumption. If you are using an RTX 3090 graphics card, we recommend the Great Wall GX650 650W with a power supply of 650W; if you are using a TRX 4090 graphics card, we recommend the SeaSonic Focus GX-850 with a power supply of 850W.

In addition to the above component modules, the AI computer host also has chassis, fans, mouse and keyboard, and display components, which are briefly described here. The chassis should be dustproof, collision-proof, beautiful, safe, and easy to route. It is recommended to use a closed chassis that can accommodate all component modules and consider GPU and CPU fan heat. The fan should match the CPU heat dissipation requirements and be silent. The mouse and keyboard should be easy to use. The display should be reused as much as possible.

Summary: After assembling the above configuration, you can assemble a high-performance AI computer that can run DeepSeek-R1-Distill-Qwen-32B for around 10,000 yuan, taking into account the application experience of games, image generation, audio generation, etc. At the same time, it is emphasized that unless you are an enthusiast, using the large model through the official website and API is the most economical and functional choice