A.5 Hardware and Cloud Resource Guide
The short answer: do not buy a GPU first. Start with the task, then choose local CPU, cloud GPU, or API.
Quick decision table
| Learning stage | Local need | Better option when stuck |
|---|---|---|
| Chapters 1-5 tools, Python, data, math, classic ML | 8-16GB RAM, SSD | Usually no GPU needed |
| Chapter 6 deep learning basics | 16GB RAM | Cloud GPU for training exercises |
| Chapter 7 LLM principles and fine-tuning concepts | 16-32GB RAM | Cloud GPU or API experiments |
| Chapters 8-9 RAG and Agent | 16GB RAM, stable network | API-first engineering route |
| Chapters 10-11 CV and NLP | 16GB RAM | Cloud GPU for heavier experiments |
| Chapter 12 multimodal | 16-32GB RAM | Cloud generation or API services |
Buying priority
For most learners, spend in this order:
- Memory: 16GB minimum, 32GB comfortable.
- SSD: 512GB minimum, 1TB comfortable.
- Stable environment: clean Python, Node, Docker, and project folders.
- Display and input comfort: external monitor, keyboard, mouse.
- GPU: only after you know your real workload.
When to use cloud or API
| Option | Best for | Watch out for |
|---|---|---|
| Free notebooks | Small demos and learning the workflow | Time limits and unstable availability |
| Hourly cloud GPU | Training experiments with clear code and data | Prepare first, shut down immediately after use |
| API-first route | RAG, Agent, assistant, and product projects | Logging, cost control, privacy, and retries |
| Local GPU | Frequent long-term training and fast local iteration | VRAM, cooling, power, and total cost |
When a local GPU is worth it
Buy only when at least two are true:
- You will train models frequently for months.
- Cloud queues or time limits slow you down every week.
- You know the model size, batch size, and VRAM you need.
- You need fast local iteration more than low upfront cost.
If the reason is only “I may need it later,” wait.
Practical plan
Use your current computer for Chapters 1-5. Rent cloud GPU when Chapter 6, 10, or 11 really needs it. Use API-first projects for Chapters 8-9. Decide on local GPU only after your project workload proves it.