Workload guide · AI
Model training / fine-tuning: hardware requirements guide
Fine-tuning and training small models (LoRA, small checkpoints).
Hardware requirements by tier
Three tiers for every workload: the minimum that works, the recommended sweet spot, and the comfortable headroom level. These are the same tiers the WisePC decision engine uses when it plans a build around your goal.
| Tier | CPU cores | RAM | Storage | GPU | Network |
|---|---|---|---|---|---|
| Minimum | 8 cores | 32 GB | 2 TB (ssd) | ai | 1 GbE |
| Recommended | 16 cores | 64 GB | 4 TB (ssd) | ai | 2.5 GbE |
| Comfortable | 24 cores | 128 GB | 8 TB (ssd) | ai | 10 GbE |
Which tier do you need?
- Pick Minimum (8 cores, 32 GB RAM) only for a single-purpose machine on a tight budget — expect little headroom.
- Recommended (16 cores, 64 GB RAM) is the sweet spot: enough for the workload plus the usual side-services, without overspending.
- Pick Comfortable (24 cores, 128 GB RAM) when this workload shares the machine with others or will grow — you pay for headroom, not for anxiety.
- GPU rule for this workload: ai.
Frequently asked questions
How much RAM does model training / fine-tuning need?
32 GB is the sensible minimum, 64 GB covers most real setups, and 128 GB gives comfortable headroom for growth and extra services.
How many CPU cores does model training / fine-tuning need?
A 8-core CPU is the minimum, 16 cores is the recommended sweet spot, and 24 cores is comfortable when it shares the machine with other workloads.
Does model training / fine-tuning need a dedicated GPU?
A dedicated GPU is strongly recommended — this workload does AI compute.
What storage and network does model training / fine-tuning expect?
Storage: 4 TB of SSD is the recommended baseline (2 TB minimum, 8 TB comfortable). Network: 2.5 GbE is the recommended baseline.
What runs well alongside model training / fine-tuning?
It pairs naturally with: AI experimentation.