個人 AI 項目如何按每週 GPU 使用時數比較租用與購買，並留意計費陷阱
Rent vs Buy a GPU for Side-Project AI: Hours/Week Math + Billing Pitfalls
這篇個人 AI 項目指南提出以每週實際 GPU 忙碌時數計算租用與購買的損益平衡點，忙碌時間不等於 SSH 連線開啟時間。
Side-project AI (ComfyUI, LoRA, small fine-tunes) often starts with “should I just buy a 4090?” The better question is: how many effective GPU-hours per week will you actually burn?
A simple break-even sketch
- Estimate capital for a used consumer flagship + PSU headroom.
- Spread over 12 months → weekly ownership tax (ignore power/noise for a moment).
- Divide by your cloud $/GPU-hour → hours/week where owning starts to win.
If your real work is bursty — a weekend LoRA, a deadline sprint — hourly rental usually wastes less money on idle silicon. Effective hours = GPU busy time, not an open SSH session.
Three billing pitfalls
- Stopped compute ≠ stopped bill. Disks often keep charging after the GPU stops. Export weights, verify, delete unused volumes.
- Data transfer eats wall-clock. Pushing large checkpoints can take longer than the job. Test with a tiny file first.
- Bare images cost setup time. Starting from a ComfyUI / PyTorch template can save a billable hour of dependency hell.
Example public hourly ranges (CNY)
Snapshot only — always trust the live console. Low→high = cheaper community-style tier → higher secure-style tier:
- RTX 4090 ≈ ¥1.19–3.12/hr
- RTX 5090 ≈ ¥2.42–4.17/hr
- H100 SXM ≈ ¥9.41–13.96/hr
- H200 SXM ≈ ¥12.56–18.52/hr
I maintain TapGPU, an hourly GPU rental console with a public pricing card wall. Disclosure: I’m involved with the product. Use the framework above on any provider.
Checklist for a clean first session
Top up → pick stock → launch a template → run one familiar mini-job end-to-end → log upload + ready + first-result wall-clock → then scale → stop GPU and clean disks.
What break-even hours/week are you using for consumer vs data-center cards?
來源：DEV Community · dev.to