How can I reduce my cloud GPU costs?

Use spot or preemptible instances, pick providers with per-minute billing, shut down idle machines, and leverage techniques like LoRA and 8-bit optimizers to fit models on cheaper GPUs.

What’s the cheapest way to prototype on A100 GPUs?

Thunder Compute offers A100 40GB GPUs for $0.57/hr and 80GB for $0.78/hr with per-minute billing and $20/month in free credit—ideal for low-cost prototyping.

Are spot GPUs safe to use for training?

Yes, if your workflow can checkpoint often. Spot GPUs can save 60–90% but may be interrupted, so they're best for short jobs or training with resume support.

What is LoRA and how does it help cut costs?

LoRA (Low-Rank Adaptation) fine-tunes models using only a small number of parameters, dramatically reducing VRAM and compute requirements—great for running on cheaper GPUs.

Why is per-minute billing better than hourly billing?

Per-minute billing prevents overpaying for unused time—especially helpful for short experiments. It can save up to 40% on bursty workloads compared to hourly billing.

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10 Tricks to Cut Your Cloud GPU Costs During Prototyping

Practical ways to keep your deep-learning budget lean without slowing iteration

Published:

May 19, 2025

Last updated:

May 19, 2025

Why cloud GPU costs balloon during prototyping

Short experiments, frequent restarts, and oversized instances add invisible dollars to every training run. The tactics below help U.S. researchers and indie devs curb waste fast, no hardware purchases required.

1. Choose Spot or Preemptible GPUs

Spot (AWS) and Preemptible (Google Cloud) GPUs rent unused capacity at 60 – 90 % off on-demand rates, perfect for fault-tolerant runs that checkpoint often. AWS Spot Instances pricing and Google Cloud spot GPU pricing list current discounts up front.

2. Pick providers with per-minute billing

If your notebook shuts down after 17 m 30 s, hourly billing means paying for 42 unused minutes. Thunder Compute and a few others bill by the minute, trimming bursty workload costs by roughly 30 – 40 %. See Thunder Compute pricing for live A100 rates.

3. Script idle shutdowns

Automate gcloud compute instances stop or aws ec2 stop-instances whenever GPU utilization drops below 10 %. A simple cron job can save hundreds of idle hours a month.

4. Right-size early with Thunder A100s

Most fine-tunes fit comfortably on a single 40 GB or 80 GB A100. Thunder’s on-demand price is $0.57 / hr (40 GB) or $0.78 / hr (80 GB); often cheaper than smaller consumer cards on other clouds. Try a quick benchmark before reaching for costlier H100s.

5. Train with mixed precision

FP16 or BF16 halves memory needs compared with FP32 and can speed math on modern GPUs. NVIDIA’s mixed-precision guide shows identical accuracy in many workloads while slashing VRAM.

6. Switch to 8-bit optimizers

The bitsandbytes 8-bit Adam optimizer cuts optimizer state in half, freeing ~50 % memory and letting you choose a smaller (cheaper) GPU. Hugging Face 8-bit docs walk through the change in five lines of code.

7. Use parameter-efficient fine-tuning (LoRA)

LoRA freezes the base model and learns tiny rank-decomposition matrices; often 0.1 % of original parameters. That means smaller batch sizes, faster epochs, and lower bills.

8. Rely on gradient accumulation

Accumulate gradients over several mini-batches to mimic a large batch on limited VRAM. You keep accuracy yet avoid renting a multi-GPU cluster.

9. Tag and alert spend early

Cloud budgets spiral when no one is watching. Enable cost-alerts at 50 %, 75 %, and 90 % of your monthly limit so you can adjust batch sizes or switch to spot capacity in time.

10. Stack every free credit

Thunder Compute gives $20 in recurring monthly credit—enough for ~35 GPU-hours on a 40 GB A100. Combine that with GitHub Classroom credits and other academic grants to offset early experiments.

Ready to try these tricks? Spin up an A100 in seconds and claim your $20 monthly credit on the Thunder Compute signup page. Happy prototyping!