Every free GPU option for training ML models — no credit card required.
| Platform | GPU | Free Hours | Credit Card | Best For |
|---|---|---|---|---|
| Kaggle | Tesla T4 (16GB) | 30 hrs/week | ❌ No | Training & notebooks |
| Google Colab | T4/V100 | ~4 hrs/session | ❌ No | Quick experiments |
| Lightning AI | T4 | 22 hrs/month | ❌ No | PyTorch Lightning |
| Paperspace Gradient | M4000 (8GB) | Limited | ✅ Yes | MLOps pipelines |
| Saturn Cloud | T4 | 30 hrs/month | ✅ Yes | Dask + ML |
| Intel DevCloud | Xeon + GPU | 120 days trial | ❌ No | Intel optimized models |
# 1. Go to kaggle.com → New Notebook → Enable GPU
# 2. Run this:
!pip install transformers datasets torch
from transformers import AutoModelForSequenceClassification, Trainer, TrainingArguments
import torch
print(f"GPU: {torch.cuda.get_device_name(0)}") # Tesla T4
print(f"VRAM: {torch.cuda.get_device_properties(0).total_mem / 1e9:.1f} GB") # 15.1 GB| Model Size | Training Time (T4) | Examples Needed | Use Case |
|---|---|---|---|
| DistilBERT (66M) | 15 min | 500-2000 | Text classification |
| BERT-base (110M) | 30 min | 1000-5000 | NER, QA |
| GPT-2 Small (124M) | 1 hr | 5000+ | Text generation |
| ViT-Small (22M) | 20 min | 1000-3000 | Image classification |
| Whisper-small (244M) | 2 hrs | 1000+ | Speech recognition |
- Use fp16 — halves memory usage, doubles training speed
- Gradient accumulation — simulate larger batch sizes without more VRAM
- Kaggle > Colab — no disconnects, 30 hrs/week guaranteed
- LoRA/QLoRA — fine-tune 7B+ models on a single T4
- Save checkpoints — Kaggle sessions can restart
- Fine-Tune ML Models for $0 — Complete training pipeline
- Awesome Web Scraping 2026 — Collect training data
- LLM Cost Calculator — Compare API vs training costs
- Prompt Engineering Guide — When prompting beats fine-tuning
- AI Tools Comparison — All AI tools benchmarked
- Full Tutorial on Dev.to
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