It can often be confusing to find the right guide to build your first AI PC or Rig. There is a huge amount of data available on the internet, which can sometimes cause confusion. In this article, I will help you on your journey to build your first AI workstation rig.
The primary aim of this article is to build an advanced workstation that also has a better price-to-performance ratio. I have analyzed multiple GPUs, especially NVIDIA GPUs from Kepler to Blackwell, as most artificial intelligence, deep learning, and machine learning applications rely heavily on GPU performance. While conducting this research, I compared the latest GPUs from the RTX 5090 to the Kepler series. With these resources, I have created a curated list of hardware that is required to build this AI workstation under $4,000.
As you know, the most important factor for any AI workstation is the GPU, and for this, I have compared a few top-performing GPUs in today’s market. This can be observed below:
GPU Half-Precision Performance
As seen in the above table, the NVIDIA H100 leads in performance for AI in half precision, but the cost of the H100 is extremely high for a general audience. Therefore, for this guide, we are going to opt for a GPU that has a good price-to-performance ratio. So, let’s go ahead with our AI/ML workstation build.
Best AI Rig under $4000
Building your first rig may not be as hard as you think. With the current rise in demand for AI, the prices of GPUs have also risen. We can also see that the prices of older and outdated GPUs have shown a significant rise in interest.
Before we go through how I selected the products, here is a quick summary of all the products:
- Dual RTX 3090
- Ryzen 9 7950X
- MSI X670E Motherboard
- CORSAIR VENGEANCE DDR5 RAM 128GB
- Samsung SSD 9100 PRO 2TB
- CORSAIR HX1500i
- CORSAIR 360mm AIO CPU Liquid Cooler
- Antec Flux Pro CPU Case
For the purpose of choosing the GPU, I compared VRAM, Sustained Token/Sec (8B Models), and price.
| GPU | VRAM | Sustained Tokens/sec* | Price (2025) | Notes |
|---|---|---|---|---|
| H100 SXM/PCIe | 80 GB | 900–1,200 | $22,000–$28,000 | Top enterprise, ECC, 3TB/s bandwidth, best for 70B+ models |
| A100 PCIe/SXM | 40/80 GB | 450–600 | $6,000–$9,000 | Data center, strong multi-model, MIG slicing, ECC ATX form, ECC, good balance, not highest raw speed |
| RTX A6000 | 48 GB | 250–350 | $4,000–$5,500 | Data center & enterprise, good VRAM, good balance, not highest raw speed |
| RTX 5090 | 32 GB | 213 | $1,999 | Newest consumer flagship, excellent VRAM and speed |
| RTX 4090 | 24 GB | 128 | $1,600–$2,000 | Consumer flagship, for 7B–13B models, high VRAM, great value |
| RTX 3090 | 24 GB | 112 | $800–$900 (used) | Best used value, 24GB VRAM, handles 7/13/30B LLM, excellent price-to-performance ratio |
| Intel Arc B580 | 12 GB | 62 | $249 | Budget option, only for small models (7B, etc.) |
| Tesla T4 | 16 GB | 60–90 | $400–$700 | Edge inference, very efficient power-wise, great for cloud deployments |
*Token/sec values based on 8B models at moderate batch/context;
1. 2 x Nvidia RTX 3090
NVIDIA GPUs lead the current race in AI computing, and I believe they will continue to do so for the next 3-4 years. After analyzing all the GPUs on the market, I believe the RTX 3090 offers the best value right now. Newer AI models have huge memory and bandwidth requirements, and I think the RTX 3090 fits perfectly as it comes with 24GB of GDDR6X memory, which is sufficient to run various AI models. For this particular build, we are going with dual RTX 3090s, and our VRAM will scale up to 48GB.
Below are the memory and performance rates of the RTX 3090 as well as their performance for FP16 and FP32. The GPU also supports 2-way NVLink, which will help us connect two NVIDIA RTX 3090 GPUs. The NVLink bridge enables high-speed, bidirectional communication between our two RTX 3090s, dramatically increasing data transfer rates compared to standard PCIe connections for AI models.
Memory Specifications
| VRAM Size | 24 GB |
| VRAM Type | GDDR6X |
| VRAM Bus | 384 bit |
| Bandwidth | 1010 GB/s |
Performance Rates
| Pixel Rate | 208.3 GPixel/s |
| Texture Rate | 625 GTexel/s |
| FP16 | 40.000 TFLOPS |
| FP32 | 40.000 TFLOPS |
| FP64 | 0.625 TFLOPS |
2. Ryzen 9 7950X
Next, we can choose the Ryzen 9 7950X as the CPU for our build. Although most of the computation for LLMs will be done with the GPU, a faster CPU is still essential to the overall workflow and system management. The Ryzen 9 7950X will be able to power most of the AI models you throw at it. While we are using NVIDIA GPUs, AMD CPUs still outperform Intel in most tasks.
The Ryzen 9 7950X comes with 16-Cores and 32-Threads at a 4.50 GHz clock speed and 5.7 GHz when unlocked. The processor also has 16 MB L2 plus 64 MB L3 cache memory for an accurate refresh rate and processing.
The processor fits in an AM5 socket and perfectly fits in our chosen motherboard MSI X670E.
3. MSI X670E Motherboard
Next up we have the MSI X670E Motherboard, which we have chosen. It is priced between $200-$250 and provides a good fit for our AI machine. Although this is a gaming motherboard, it is exactly the motherboard that is compatible with our dual RTX 3090 GPUs for AI processing.
The motherboard comes with support for AM5, DDR5 RAM, and PCIe 5.0. We are going to make sure to use most of its latest features to power up our AI rig.
4. CORSAIR VENGEANCE DDR5 RAM 128GB
As most of the tasks carried out with our PC build will be memory-heavy, we will also be needing much more RAM to keep the AI models processing.
Although 64GB of RAM would be most optimal, we will be going with 128GB of DDR5 RAM from Corsair.
These are 32GB DDR5 RAM x 4 that would make our total RAM capacity at 128GB. With this amount of RAM, you can easily run a 30B LLM.
5. Samsung SSD 9100 PRO 2TB
6. CORSAIR HX1500i
Next up, and the most important PC part to power up our rig, is the PSU. With these many high-end PC parts, we will require an ample amount of PSU wattage to power our rig.
Here is a rough calculation of the power required:
| Component | Stock Power (W) | OC / Peak Power (W) |
|---|---|---|
| 2 × RTX 3090 GPUs | 700 (350×2) | 840 (420×2) |
| Ryzen 9 7950X CPU | 170 | 230 |
| MSI X670E Motherboard | 60 | 60 |
| 128GB DDR5 RAM | 20 | 20 |
| Samsung 9100 PRO 2TB NVMe SSD | 8 | 8 |
| Corsair 360mm AIO (pump + 3 fans) | 15 | 15 |
| Case fans, peripherals, misc. | 30 | 30 |
| Subtotal (system draw) | 1003 W | 1203 W |
| Headroom (20% stock / 30% OC) | +201 W | +361 W |
| Recommended PSU Wattage | 1200–1300 W | 1600 W |
So, as per my calculation, with 30% headroom, we would require a 1600W PSU, but for a better price, we can go with a 1500W PSU as well. I recommend you go with the Corsair HX1500i, as it has better value and good reviews on the market.
7. CORSAIR 360mm AIO CPU Liquid Cooler
With the PC powering AI models, it’s also important to have better cooling for the system. By system, I mean the CPU as well as other parts. But when it comes to CPU cooling, it is always better to go with a liquid cooler. Coolers such as the Corsair 360mm AIO CPU Liquid Cooler help the CPU maintain its optimum temperature and avoid bottlenecking, leading to sustained performance.
8. Antec Flux Pro CPU Case
This Full-Tower E-ATX PC Case comes with optimal space for most of our specifications. The case is a Full Tower E-ATX, which can fit our dual RTX 3090s and our AIO CPU Cooler. But there will be a need for space adjustments.
The PC Case already comes with 12 Fans, and I guess it is more than enough to provide ventilation and keep our rig cool.
TOTAL ~ $3,931.93 (Prices May Change)
