Description
Powered by Ryzen Threadripper PRO 9985WX 64C 3.2GHz, this configuration sustains demanding AI workflows without throttling.
Store massive training sets locally with 8TB NVMe SSD (2x4TB RAID) and maintain zero-latency iteration cycles.
This curated workstation profile stays quiet under load while delivering the acceleration modern AI production cycles expect.
Configuration Overview
- CPU: Ryzen Threadripper PRO 9985WX 64C 3.2GHz
- GPU: Dual RTX PRO 6000 Blackwell 96GB (192GB Total)
- Memory (RAM): DDR5 256GB ECC (8x32GB)
- NVMe Storage: 8TB NVMe SSD (2x4TB RAID)
- SATA Storage: 4TB HDD
- Motherboard: Threadripper Pro / ECC Ready
- Chassis: Enthoo Pro 2 Server Edition Case
- Operating System: Windows 11 Pro Workstation edition
- Warranty: 1 Year Warranty
Frequently Asked Questions
Is this AI workstation good for deep learning and model training?
Yes. This workstation is engineered for demanding AI workloads with NVIDIA RTX GPUs, high-wattage power delivery, and validation against TensorFlow and PyTorch benchmarks.
Why choose an 8TB NVMe SSD for AI and data science workloads?
Large NVMe capacity keeps massive datasets, checkpoints, and logs on-device for faster iteration. 8TB is ideal when you work with multi-terabyte corpora or rotating experiment branches.
Can this workstation handle rendering or simulation tasks in addition to AI workloads?
Absolutely. High-end GPUs accelerate Blender Cycles, Unreal Engine, and GPU-enabled video workflows, making the system ideal for teams that blend AI and creative production.
Does this AI workstation support Ubuntu and popular AI frameworks on first boot?
Yes. We validate Ubuntu LTS and Windows 11 Pro with CUDA, cuDNN, and PyTorch/TensorFlow toolchains so you can begin training immediately.
How loud is the system during long training sessions?
Tuned fan curves, liquid CPU cooling, and a high-airflow chassis keep acoustics steady. The system delivers airflow noise without high-pitch whine.
Will dual RTX 5090 GPUs help with large language models?
Yes. Two RTX 5090s speed finetuning, increase effective VRAM for tensor/pipeline parallelism, and boost throughput for inference services.











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