Machine Learning Workstations Engineered for Production ML
Purpose-built hardware for every stage of the ML pipeline. GPU VRAM matched to your model architecture, fast NVMe for multi-terabyte datasets, and validated software stacks for TensorFlow, PyTorch, JAX, and the full ML ecosystem.
Hardware for Every ML Stage
Each pipeline stage bottlenecks on different resources. We design hardware that eliminates constraints across the full workflow.
Training & Fine-Tuning
- RTX 5090 (32 GB) for models up to 13B params at full precision
- RTX PRO 6000 (96 GB) for single-GPU training of 70B+ models
- LoRA, QLoRA, and full fine-tuning environments pre-configured
- ECC DDR5 on Threadripper PRO for training stability
Data Science & Analytics
- Ryzen 9950X3D with 144 MB cache for pandas and feature engineering
- 128 GB to 512 GB DDR5 for in-memory dataset processing
- RAPIDS cuDF and cuML for 10x to 100x GPU-accelerated analytics
- Gen5 NVMe at 14 GB/s for dataset streaming during training
ML Workstation Builds
Specialized configurations for every ML discipline.
Deep Learning Training
GPU-optimized builds for CNNs, transformers, and diffusion models. VRAM capacity matched to your model architecture and batch size requirements.
LLM Fine-Tuning
Configured with Unsloth, Hugging Face TRL, or Axolotl. System RAM sized at 2x to 4x GPU VRAM for mixed-precision training with CPU offloading.
Computer Vision
High-bandwidth GPU paired with fast NVMe for image dataset loading. YOLO, Detectron2, and custom architectures pre-validated.
Classical ML & Data Science
CPU and memory optimized for scikit-learn, XGBoost, and feature engineering. GPU acceleration with RAPIDS for structured data workloads.
AMD ROCm Workstations
Production-viable alternative to NVIDIA. Radeon PRO W7900 (48 GB) validated with PyTorch and vLLM on our own infrastructure daily.
MLOps & Experiment Management
Pre-configured with MLflow, DVC, Docker, and Jupyter. Tiered storage for active experiments, model registries, and dataset archives.
ML Workstation vs. Cloud GPU
One-Time Purchase
A custom ML workstation costs $10,000 to $14,000. The equivalent cloud A100 reserved instance totals $121,842 over 36 months. You own the hardware and keep the savings.
Hybrid Approach
Custom workstations handle daily development at fixed cost. Cloud instances provide burst capacity for periodic large-scale training runs. Most teams save 80% to 90% on compute.
How We Build Your ML Workstation
ML pipeline assessment and bottleneck analysis
Component specification matched to your models
Assembly with validated cooling for sustained loads
72-hour burn-in under real ML workloads
Framework validation: PyTorch, TensorFlow, JAX tested end-to-end
Delivery with ongoing support and upgrade path
Frequently Asked Questions
How much does an ML workstation cost?
Builds range from $5,000 for a data science focused build to $35,000 for multi-GPU deep learning rigs. Most configurations pay for themselves in 6 to 10 weeks compared to equivalent cloud GPU spend.
What GPU do I need for my model size?
QLoRA fine-tuning of a 7B model fits in 16 GB. A 13B model needs 24 GB. A 70B model requires 48 GB+ for QLoRA or 192 GB+ for full fine-tuning. We analyze your specific model architecture to recommend the right GPU.
Do you support AMD ROCm workstations?
Yes. We build and validate ROCm workstations daily on our own infrastructure. Radeon PRO W7900 (48 GB), RX 7900 XTX (24 GB), and AMD Instinct accelerators with ROCm 6.x and tested PyTorch installations.
What software comes pre-installed?
Your complete ML stack: TensorFlow, PyTorch, JAX, scikit-learn, XGBoost, RAPIDS, Jupyter, and your preferred tools. CUDA/ROCm versions validated against all framework dependencies before delivery.
Can you build multi-workstation clusters?
Yes. We design workstation clusters connected via 10GbE or 25GbE with shared storage, SLURM scheduling, and distributed training support for teams that need more compute than a single workstation provides.
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Ready to Build Your ML Workstation?
Get a custom specification with component rationale matched to your pipeline requirements.