What GPU Specs Do You Need for AI Model Training?
Different scales of AI models require different GPU configurations. This article helps you choose according to your needs.
Choosing GPU by Model Size
| Model Scale | Parameters | Recommended GPU | Memory Requirement |
|---|---|---|---|
| Small Model | < 1B | RTX 4090 | 24 GB |
| Medium Model | 1B - 7B | A100 40GB | 40-80 GB |
| Large Model | 7B - 70B | H100 80GB | 80 GB × Multi-card |
| Massive Model | > 70B | H200 / B200 | 141-192 GB |
Memory is Key
GPU memory determines how large a model you can train:
- 24 GB: Can fine-tune 7B parameter models (using LoRA/QLoRA)
- 80 GB: Can fully train 7B models, fine-tune 70B models
- 141 GB (H200): Can train 70B+ models, larger batch sizes
Multi-GPU Training Considerations
When single-card memory is insufficient, multi-GPU parallelism is needed:
- Data Parallelism: Multiple cards process different batches
- Model Parallelism: Model split across multiple cards
- NVLink Bandwidth: Critical for inter-card communication performance
H100/H200's 900 GB/s NVLink provides excellent multi-card scalability.
KONST's GPU Options
| GPU | Memory | Suitable Scenarios | Price |
|---|---|---|---|
| RTX 4090 | 24 GB | Inference, small model fine-tuning | $0.49/hr |
| A100 SXM4 | 80 GB | Medium model training | $1.20/hr |
| H100 SXM5 | 80 GB | Large model training | $2.96/hr |
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