Customer Story: Machine Learning with Nvidia GeForce RTX 3060 Ti

I am a machine learning enthusiast who wanted to train AI models efficiently using GPU acceleration. After researching several providers, I decided to go with Database Mart's Dedicated Server with an NVIDIA GeForce RTX 3060 Ti. It turned out to be a great choice — super stable, affordable, and easy to set up. With Windows, WSL2, and Ubuntu, I can run TensorFlow and JupyterLab without any issues, even for long training sessions.

*Submitted by user "bata***asir@gmail.com"*

Application Scenario

I use the Database Mart NVIDIA GeForce RTX 3060 Ti GPU Server mainly for machine learning and AI model training. With Windows OS, WSL2, and Ubuntu set up, I run TensorFlow and JupyterLab to handle data processing, model development, and long training sessions that fully utilize the GPU.

Server Specifications

✅ CPU: Dual 12-Core Intel Xeon E5-2697 v2
✅ RAM: 128GB
✅ Storage: 240GB SSD + 2TB SSD
✅ Operating System: Windows
✅ GPU: NVIDIA GeForce RTX 3060 Ti
✅ Microarchitecture: Ampere
✅ CUDA Cores: 4864
✅ Tensor Cores: 152
✅ GPU Memory: 8GB GDDR6
✅ FP32 Performance: Up to 16.2 TFLOPS

Deployment Process

For the deployment process, I followed a straightforward setup guided by ChatGPT.

• Step 1: Environment Setup
  I started by installing the CUDA driver, enabling WSL2, and setting up Ubuntu on Windows. This created a stable GPU-enabled environment for my machine learning tasks.

• Step 2: Software Installation
  Next, I installed TensorFlow, JupyterLab, and the necessary Python modules inside Ubuntu. These tools allowed me to build and run my training workflows efficiently.

• Step 3: System Optimization
  I adjusted the WSL2 configuration to allocate 4 CPU cores and 16GB of RAM, improving performance during model training.

• Step 4: Testing and Verification
  Finally, I verified that TensorFlow detected the NVIDIA GeForce RTX 3060 Ti GPU correctly. Everything ran smoothly, and I was able to train models continuously without any issues.

Performance Review

The server has been running continuously for over 12 hours without any crashes or forced restarts, maintaining consistent performance. Overall, the server delivers reliable GPU acceleration, fast data processing, and stable operation, making it perfectly suited for machine learning and AI development tasks.

Network Performance

Reliability Evaluation

The server has proven to be highly reliable throughout my usage. It has been running continuously for over 12 hours without any crashes, forced restarts, or performance drops.

Resource Utilization (Under Load)

✅ CPU Usage: 3%
✅ GPU Usage: 21%
✅ RAM Usage: 11%

Application Performance Evidence

Optimization Tips

To improve performance, I adjusted the WSL2 configuration to allocate 4 CPU cores and 16GB of RAM, which significantly enhanced processing speed during model training. Beyond that, no major tweaks were needed — the system worked efficiently out of the box. My main advice is to fine-tune resource allocation based on your workload to ensure smoother GPU utilization and faster training performance.

Conclusion & Recommendations

"My conclusion is the server ready to run any tasks without much modification."

Overall, my experience with the Database Mart Dedicated RTX 3060 Ti GPU Server has been excellent. The server runs smoothly, remains stable during long machine learning tasks, and delivers consistent GPU performance without needing complex configuration.

I would recommend this setup to anyone working on AI training or machine learning projects who needs a powerful yet affordable GPU server. It’s easy to deploy, performs reliably, and is ready to handle demanding workloads right out of the box.

Why DBM?

"For dedicated server with GPU it's affordable price. Almost all other providers only as far as dedicated server without GPU for the same price."

I chose Database Mart because it offers affordable GPU-powered dedicated servers that are hard to find elsewhere at the same price point. Most other providers only offer non-GPU servers for similar costs, but DBM includes powerful GPUs like the RTX 3060 Ti, making it a great value for machine learning workloads.

In addition to competitive pricing, DBM provides a stable Windows remote environment, reliable uptime, and excellent performance. The setup process was straightforward, and everything worked smoothly without technical issues — making it an ideal choice for users who need both power and simplicity.

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