Research & Fine-Tuning Workstation
A workstation for training experiments and fine-tuning, where memory, sustained cooling and dataset storage matter most.
Status: Concept: specification subject to validation
Form factor: Full tower or deskside workstation, single or dual GPU
Price: On request
Reviewed 27 Oct 2026
Who it is for
University and research groups, ML engineers running experiments.
- Fine-tuning
- Local LLM
In the advisor, this build serves the Personal or creative workstation category.
Configuration options
Ranges we choose from during validation. The final specification depends on your workload, software and environment.
| Part | Options |
|---|---|
| CPU | Workstation-class processor with enough PCIe lanes for one or two GPUs and fast storage |
| GPU | One or two professional GPUs; total VRAM chosen from model size, precision and batch size |
| Memory | 128 to 512 GB ECC, sized to data loading and preprocessing |
| Storage | Fast NVMe scratch for datasets and checkpoints, plus bulk storage |
Cooling, power and installation
Cooling
Active-cooled GPUs; sustained training loads are validated during burn-in.
Power
Two high-end GPUs under sustained load need a power and cooling plan; this is checked before quoting.
Chassis
Workstation chassis with airflow for sustained full load and space for two dual-slot cards.
Network
10 GbE recommended for datasets on shared storage.
Software examples
Examples we can install and configure. Compatibility is confirmed for your exact versions during validation, not assumed.
- PyTorch with CUDA or ROCm, matched to the GPU
- Hugging Face tooling
- Experiment tracking of your choice
Limits, assumptions and open questions
Limitations
- Full fine-tuning of large models may exceed one machine; parameter-efficient methods change the sizing.
- Framework support differs between CUDA and ROCm; confirmed per project.
Assumptions
- Linux preferred
- One to three researchers
- Datasets fit on local storage
What we will ask you
- Model size and precision?
- Full fine-tuning or parameter-efficient?
- Dataset size?
Start from the Research & Fine-Tuning Workstation
Tell us your workload and we will adapt this configuration, check compatibility, and quote it. No obligation.