Multi-GPU Systems for Large-Scale AI

AI Training Workstations

Up to 384GB of GPU VRAM for large-scale model training. 4x NVIDIA RTX PRO 6000 Blackwell GPUs with 5th-generation Tensor Cores. Built, configured, and deployed by Petronella Technology Group.

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What AI Training Demands from Hardware

Training AI models is the most computationally intensive workload in modern computing. Here is what your hardware needs to deliver.

Multi-GPU Parallelism

Training splits across multiple GPUs using data parallelism and model parallelism. 4x GPUs can reduce training time by up to 3.8x compared to a single GPU.

Massive GPU VRAM

Model parameters, gradients, and optimizer states all reside in VRAM. A 7B parameter model needs 56GB+ in FP16 for training. 384GB lets you train much larger models.

Fast GPU Interconnect

PCIe Gen5 bandwidth ensures rapid data transfer between GPUs during distributed training. Minimized communication overhead means maximum training throughput.

AI Training Workstation Lineup

Every system features 4x NVIDIA RTX PRO 6000 Blackwell GPUs with 5th-generation Tensor Cores, Twin NVMe storage, and 32GB DDR5 system memory.

Desktop Tower 384 GB GPU VRAM

Threadripper 9000 AI Training Workstation

High core-count desktop for solo researchers and small teams

CPUAMD Threadripper 9970X
GPU4x RTX PRO 6000 Blackwell MaxQ 96GB
Total VRAM384 GB GDDR7 ECC
System RAM32 GB DDR5
StorageTwin NVMe
Call for Pricing: (919) 348-4912
Most Popular
Desktop Tower 384 GB GPU VRAM

Threadripper Pro 9000 AI Training Workstation

Full-width PCIe lanes for maximum GPU-to-GPU bandwidth

CPUAMD Threadripper PRO 9975WX
GPU4x RTX PRO 6000 Blackwell 96GB
Total VRAM384 GB GDDR7 ECC
System RAM32 GB DDR5
StorageTwin NVMe
Call for Pricing: (919) 348-4912
Desktop Tower 384 GB GPU VRAM

Xeon AI Training Workstation

Intel platform for ISV-certified enterprise environments

CPUIntel Xeon W7-3565X
GPU4x RTX PRO 6000 Blackwell 96GB
Total VRAM384 GB GDDR7 ECC
System RAM32 GB DDR5
StorageTwin NVMe
Call for Pricing: (919) 348-4912
Rackmount 384 GB GPU VRAM

Threadripper 9000 AI Training 384B Rack Workstation

Data center-ready 4-GPU training in standard 19" rack form factor

CPUAMD Threadripper 9970X
GPU4x RTX PRO 6000 Blackwell 96GB
Total VRAM384 GB GDDR7 ECC
System RAM32 GB DDR5
StorageTwin NVMe
Call for Pricing: (919) 348-4912

What Can You Train?

With up to 384GB of GPU VRAM and 5th-generation Tensor Cores, these workstations handle the most demanding AI training workloads.

Large Language Models

Fine-tune and train LLMs up to 13B parameters in full precision. Run LoRA/QLoRA on models up to 70B+.

Computer Vision

Train object detection, segmentation, and classification models on massive image datasets at high resolution.

Natural Language Processing

Train custom NLP models for text classification, named entity recognition, sentiment analysis, and translation.

Generative AI

Train and fine-tune image generation (Stable Diffusion, FLUX), video generation, and audio synthesis models.

Side-by-Side Comparison

All four systems deliver 384GB of GPU VRAM. Choose your platform based on PCIe topology, form factor, and ecosystem preference.

Specification Threadripper 9000 Threadripper Pro 9000 Xeon W7 Rack (TR 9000)
CPUThreadripper 9970XThreadripper PRO 9975WXXeon W7-3565XThreadripper 9970X
GPU Config4x RTX PRO 6000 MaxQ4x RTX PRO 60004x RTX PRO 60004x RTX PRO 6000
Total VRAM384 GB384 GB384 GB384 GB
GPU TDP per Card300W (MaxQ)600W600W600W
AI Performance4x 3,511 TOPS4x 4,000 TOPS4x 4,000 TOPS4x 4,000 TOPS
System RAM32 GB DDR532 GB DDR532 GB DDR532 GB DDR5
StorageTwin NVMeTwin NVMeTwin NVMeTwin NVMe
Form FactorDesktop TowerDesktop TowerDesktop TowerRackmount
Best ForPower-efficient 4-GPUMaximum bandwidthEnterprise/ISVData center
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More Than Hardware

Petronella Technology Group delivers a complete AI deployment, not just a box. Since 2002, we have served over 2,500 clients.

Full Deployment Service

We handle site assessment, power planning, system assembly, burn-in testing, OS installation, CUDA toolkit, PyTorch/TensorFlow setup, and network configuration.

CMMC & HIPAA Compliance

Our entire team holds CMMC-RP certification. We deploy AI workstations with compliant configurations for defense contractors and healthcare organizations.

AI Consulting

Not sure what you need? Our consultants assess your AI workload, recommend the right configuration, and help you plan for future scaling.

Frequently Asked Questions

How much GPU VRAM do I need for AI training?
VRAM requirements depend on model size, batch size, and precision. As a general guide: small models (under 1B parameters) need 24-48GB. Medium models (1-7B) need 96-192GB. Large models (7B+) benefit from 384GB across 4 GPUs. For fine-tuning with LoRA or QLoRA, you can work with significantly less VRAM. Call us at (919) 348-4912 to discuss your specific workload.
What is the difference between desktop and rackmount AI training workstations?
Desktop towers sit beside your desk and are ideal for individual researchers and small teams. Rackmount systems slide into standard 19" server racks and are built for data centers, shared computing environments, and 24/7 operation with redundant cooling. Both deliver the same GPU performance. The rackmount form factor is better when you need remote management, multiple systems in one location, or integration with existing server infrastructure.
What power requirements do multi-GPU AI workstations need?
A 4x NVIDIA RTX PRO 6000 Blackwell workstation (full-size cards at 600W each) can draw up to 2,400W from the GPUs alone, plus CPU and system overhead. We recommend a dedicated 20A/240V circuit and a UPS rated for at least 3,000VA. The MaxQ variant draws 300W per GPU, significantly reducing power requirements. Our deployment team performs a power assessment at your site before installation.
Can I use these workstations for both training and inference?
Absolutely. Every AI training workstation handles inference as well. With 384GB of combined GPU VRAM, you can run the largest open-source models at full precision or serve multiple models concurrently. Many clients train during off-hours and run inference during the workday, maximizing hardware utilization.
Do you offer CMMC and HIPAA compliant AI deployments?
Yes. Petronella Technology Group is a CMMC Registered Practitioner Organization. Craig Petronella (CMMC-RP, CCNA, CWNE, DFE #604180) and our team members Blake Rea, Justin Summers, and Jonathan Wood all hold CMMC-RP certification. We deploy AI workstations with encrypted storage, access controls, audit logging, and network segmentation to meet CMMC 2.0 and HIPAA requirements.
What is the advantage of NVIDIA RTX PRO 6000 Blackwell over consumer GPUs?
The NVIDIA RTX PRO 6000 Blackwell features 96GB of ECC GDDR7 memory (vs. 24-32GB on consumer cards), 24,064 CUDA cores, 5th-generation Tensor Cores delivering 4,000 AI TOPS, enterprise drivers with long-term support, and validation for 24/7 operation. Multi-GPU configurations with full PCIe bandwidth are only supported on professional GPUs. See full GPU specifications.
How long does it take to receive a custom AI training workstation?
Build times typically range from 2-4 weeks depending on GPU availability. Every system goes through full assembly, extended burn-in testing, OS installation, GPU driver configuration, and AI framework setup (PyTorch, TensorFlow, CUDA toolkit). We can also pre-install your specific models and datasets. Call (919) 348-4912 for current lead times.

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Ready to Train AI at Scale?

Talk to our AI hardware specialists about the right training workstation for your workload. Custom configurations available.

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