Training LLMs? Fine-tuning models? Here's which GPU you actually need (and which ones are overkill).
GPU Requirements by Model Size
Small Models (7B-13B parameters)
Examples: Llama 3.1 8B, Mistral 7B, Phi-3
Recommended: RTX 4090 (24GB)
- Cost: ₹73/hr
- Training time: 2-4 hours
- Why: 24GB VRAM is enough. No need for expensive datacenter GPUs.
Medium Models (30B-70B parameters)
Examples: Llama 3.1 70B, Mixtral 8x7B
Recommended: A100 80GB
- Cost: ₹173/hr
- Training time: 8-12 hours
- Why: 80GB VRAM handles full model. Faster than multi-GPU setups.
Large Models (100B+ parameters)
Examples: Llama 3.1 405B, GPT-4 scale
Recommended: H100 80GB
- Cost: ₹583/hr
- Training time: 24+ hours
- Why: Only option for models this large. Worth the cost.
Fine-Tuning vs Full Training
Fine-Tuning (LoRA/QLoRA)
Cheaper, faster, good enough for most use cases.
- 7B model: RTX 3090 (₹35/hr)
- 13B model: RTX 4090 (₹73/hr)
- 70B model: A100 (₹173/hr)
Total cost: ₹200-500 per model
Full Pre-Training
Expensive, slow, only if you need it.
- 7B model: A100 (₹173/hr × 100hrs)
- 13B model: A100 (₹173/hr × 200hrs)
- 70B model: H100 (₹583/hr × 500hrs)
Total cost: ₹17K-2.9L per model
Real-World Example
Case Study: Fine-tuning Llama 3.1 8B
- GPU Used: RTX 4090
- Training Time: 3 hours
- Total Cost: ₹219
- Dataset: 10K examples
- Method: LoRA
- Result: Production-ready model
Common Mistakes
Using H100 for Small Models
Don't rent H100 (₹583/hr) for 7B models. RTX 4090 (₹73/hr) works fine. You're wasting ₹510/hr.
Not Using LoRA
Full fine-tuning costs 10x more. LoRA gives 95% of the results at 10% of the cost.
Start Small, Scale Up
Start with RTX 4090. If it's too slow, upgrade to A100. Don't overpay from day one.
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