Our current turnaround time is 7-10 working days
What are you building for?
Tailored recommendationsPC builds across the site will be filtered to match your chosen use-case.

Machine Learning & Data Science

PyTorch & TensorFlow training workstations — for the work that doesn’t fit in the cloud.

ECC memory, dual-GPU options and the I/O bandwidth to keep a training run fed. Built for ML engineers and data scientists who need reproducible performance and on-prem control.

  • ECC RAM by default on Studio+
  • Up to 192GB VRAM (dual RTX 6000 Pro)
  • Threadripper PRO 8-channel memory
  • 5-year warranty
Configure NowTalk to a Specialist

Recommended configurations

Three calibrated starting points. Every component is configurable in our builder.

ML Engineer

Single-GPU training & experimentation

£4,499

Starting price

CPU
AMD Ryzen 9 9950X
GPU
NVIDIA RTX 5090 (32GB)
RAM
128GB DDR5
Storage
2TB Gen5 NVMe + 4TB NVMe
Best for
Single-GPU training, prototyping
Configure ML Engineer

Most popular

ML Studio

Professional single-GPU, ECC memory

£8,999

Starting price

CPU
AMD Threadripper PRO 7965WX
GPU
NVIDIA RTX 6000 Pro Blackwell (96GB)
RAM
128GB DDR5 ECC
Storage
4TB Gen5 NVMe + 8TB NVMe
Best for
Reproducible training, long runs
Configure ML Studio

ML Production

Dual-GPU, data-team workstation

£17,999

Starting price

CPU
AMD Threadripper PRO 7995WX
GPU
2× NVIDIA RTX 6000 Pro Blackwell
RAM
256GB DDR5 ECC
Storage
4TB Gen5 + 16TB NVMe
Best for
Multi-GPU training, large datasets
Configure ML Production

GPU vs workload (training)

Training VRAM scales with batch size, sequence length and precision. Mixed precision (bf16 / fp8) and gradient checkpointing reduce VRAM but increase wall-clock time. Figures below are rough sizing guidance.

GPU vs workload (training)
 WorkloadPractical VRAMRecommended tier
Computer vision (CNN/ViT)
16–24GBML Engineer
Tabular / sklearn / XGBoost
CPU + 16GBML Engineer
Fine-tune 7B LLM (LoRA)
24GBML Engineer
Fine-tune 13B LLM (LoRA)
40GBML Studio
Fine-tune 70B LLM (QLoRA)
80GB+ML Studio / Production
Multi-GPU distributed training
2× 96GBML Production

Why CREATE PCs

ECC RAM by default on Studio+

Single-bit memory errors silently corrupt training runs. Every Studio and Production tier ships with registered ECC DDR5.

I/O that keeps the GPU fed

Gen5 NVMe primary, large secondary NVMe for datasets. Optional 10GbE for shared dataset stores.

Linux-first if you need it

Ubuntu LTS preinstalled with CUDA, cuDNN, conda and your chosen framework. Full warranty either way.

ML Training Workstations FAQs

Workstation or cloud — which is cheaper for training?

A simple rule of thumb: if you train more than ~6 hours a day on a single high-end GPU, on-prem typically pays back inside 12 months versus equivalent cloud pricing. Add the convenience of fast local data access and reproducibility, and most ML teams keep a serious workstation for daily work and burst to cloud for huge runs.

Do you support Linux for training?

Yes — Ubuntu 22.04 / 24.04 LTS is supported with CUDA, cuDNN, conda and PyTorch / TensorFlow preinstalled on request. Warranty is identical to Windows builds.

Why Threadripper PRO over Ryzen 9?

Two reasons: 8-channel ECC memory (vs 2-channel non-ECC on AM5), and far more PCIe lanes — important for dual GPUs, fast NVMe RAID and 10GbE simultaneously. For single-GPU workstations Ryzen 9 9950X is plenty; once you go dual GPU or want ECC, Threadripper PRO is the right answer.

Can I run two RTX 6000 Pro cards in one chassis?

Yes — we build dual-RTX-6000-Pro stations in workstation cases with 1600W Platinum PSUs and validated chassis airflow. Sustained dual-GPU loads are tested for 24 hours before dispatch.

Do you offer 10GbE / Thunderbolt for shared storage?

Yes. We can spec 10GbE on Threadripper PRO builds and Thunderbolt 4 on Intel-based builds for connection to NAS / DAS dataset stores.