Custom AI Workstations for Machine Learning & AI Development
Purpose-built desktop workstations with NVIDIA RTX 5090, RTX PRO 6000, and AMD GPUs. Every component hand-selected for your AI workflow, validated under sustained load, and shipped production-ready with your full software stack.
PTG Custom vs. OEM Workstations
Every component selected for sustained AI performance, not generic office benchmarks.
PTG Custom Build
- Up to 4 GPUs with NVLink, RTX 5090 to RTX PRO 6000 Blackwell (96 GB)
- Cooling engineered for sustained 100% GPU utilization, not acoustic optimization
- Full BIOS access, unrestricted firmware, any component upgrade path
- 72-hour burn-in under real AI workloads before delivery
Security Built In
- Full-disk encryption, TPM 2.0, BIOS-level passwords, secure boot
- HIPAA, CMMC, SOC 2, and NIST 800-171 compliant configurations
- Air-gapped builds available for classified and ITAR environments
- Hardened OS images with audit-ready documentation
Available GPU Configurations
From budget inference builds to professional multi-GPU training rigs.
LLM Training & Inference
Train models up to 30B parameters quantized. Exceptional tokens-per-second for production inference workloads.
Large Model Development
Single-GPU training of 70B+ parameter models. The professional-grade choice for research and enterprise AI teams.
Development & Prototyping
Strong price-to-performance for AI application development, medium model training, and inference testing.
AMD ROCm Workloads
Vendor diversification with production-viable ROCm support. Validated with PyTorch and vLLM on our own infrastructure.
What We Build For
LLM Fine-Tuning
Fine-tune Llama, Mistral, and Qwen on proprietary data. Configured with LoRA, QLoRA, and Unsloth environments.
Computer Vision
Object detection, image segmentation, and medical imaging. High GPU bandwidth paired with fast NVMe for dataset loading.
Data Science & Analytics
GPU-accelerated RAPIDS, large-scale feature engineering with 256GB+ RAM, and Jupyter environments pre-configured.
Defense & Classified AI
Air-gapped workstations for CMMC, ITAR, and SCIF environments. FIPS 140-3 TPM, disabled wireless, offline model repos.
How We Build Your Workstation
Workload analysis and component specification (start with an AI readiness assessment)
Component sourcing and procurement
Assembly with validated cooling and power delivery
72-hour burn-in under sustained AI workloads
Security hardening and software stack installation
Delivery with lifetime upgrade support
Frequently Asked Questions
How much does a custom AI workstation cost?
Builds range from $5,000 for a single-GPU development workstation to $35,000 for a multi-GPU professional rig with RTX PRO 6000 Blackwell. Most custom builds pay for themselves in 6 to 10 weeks compared to equivalent cloud GPU spend.
Custom workstation vs. cloud GPU: which is better?
For sustained daily use, a custom workstation delivers 7x to 10x better economics over 36 months compared to cloud GPU instances. Cloud remains valuable for burst capacity. We recommend a hybrid approach for most teams.
What CPU platform should I choose?
AMD Ryzen 9950X3D excels at data pipeline operations with 144 MB cache. Threadripper PRO handles multi-GPU builds with 128+ PCIe lanes. Intel Xeon W provides ECC memory for mission-critical training stability.
Can you build HIPAA-compliant AI workstations?
Yes. Every build can include full-disk encryption, TPM 2.0, secure boot, disabled network interfaces for air-gapped operation, and audit documentation meeting HIPAA, CMMC, and SOC 2 requirements.
What software comes pre-installed?
Your complete AI stack validated end-to-end: CUDA or ROCm, PyTorch, TensorFlow, vLLM, Jupyter, and your preferred frameworks. We test the full dependency chain so you start working on day one.
Do you support workstations after delivery?
Direct engineer access for the life of the machine. No call centers, no tier-1 scripts. When your needs change, we upgrade GPU, memory, or storage in-place without voiding warranties.
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Ready to Build Your AI Workstation?
Get a custom specification with component rationale, performance projections, and cloud cost comparison.