Training and running ML models locally requires massive amounts of RAM for dataset processing, fast NVMe storage for data loading pipelines, and ideally a dedicated GPU. Local development, testing, and inference benefit hugely from laptop upgrades.
Essential Upgrades for ML Engineers
| Upgrade | Why | Cost |
|---|---|---|
| RAM: 32GB → 64GB DDR5 | Larger datasets in memory | ~£130 |
| SSD: 2-4TB NVMe Gen 4 | Store training data locally | ~£150-250 |
Storage Strategy
Keep active datasets on internal NVMe for maximum data loading speed. Archive completed projects to external storage. For large datasets exceeding 2TB, consider a NAS with 10GbE networking.
Frequently Asked Questions
Can I train ML models on a laptop?
Yes — for small to medium models, fine-tuning, and inference, a laptop with 64GB RAM and a discrete GPU (RTX 4060+) is capable.
Is an eGPU worth it for ML?
With Thunderbolt 4, an eGPU provides desktop-class performance at home. Expect 15-20% loss vs desktop due to bandwidth.
