What if your lab has no GPU? A complete guide for students to rent GPU cards

Every model trainer’s GPU rental journey begins with a mentor’s words: “Figure it out yourself.” This guide condenses the entire process into 5 steps — read and act immediately.

Step 1: Determine the GPU size you need

One GPU Suffices for Most Course Labs and Paper Reproductions RTX 3090（24GB） That’s enough: SD image generation, LoRA fine-tuning for 7B models, and quantized inference for 13B models all fall well within its capability range. Only full-parameter fine-tuning or 70B-class models truly require A100-level hardware. Still unsure? Try ourPicker, and tasks auto-generate answers.

Step 2: Choose a platform by usage model

Run Only a Few Hours Daily → ChooseBilled by the second; no charge when powered offOn platforms like AutoDL and Gpushare, remember to shut down instances when done — this is the #1 cost-saving rule. For multi-day continuous runs, compare daily/weekly bundled pricing (MatPool, MornAI, Suanjiayun).

Step 3: Maximize New User Benefits

Nearly all platforms offer sign-up bonuses: AutoDL (¥10 coupon), Gpushare (¥100 package + 95% student discount), MatPool (2 free hours), Compshare (¥5 credit).We recommend registering on 3–4 platformsUse whichever platform has available GPUs—diversifying across platforms also hedges against spot unavailability.

Step 4: Avoid These Three Pitfalls

① Forgetting to shut down: On pay-as-you-go platforms, sleeping without shutting down = burning money all night; ② Storage fees: Data disks may continue accruing charges after shutdown—policies vary per platform (see individual billing docs); ③ Spot instances: cheaper but subject to immediate preemption—avoid for training without checkpointing.

Step 5: Control your budget

Take RTX 3090 as an example: current monitored price:

Assuming 4 hours/day usage at ¥1.6/hour tier, monthly cost ≈ ¥190— Less than the cost of a hotpot meal, far more cost-effective than buying a GPU ($15,000) or renting a full server ($800+/month).