LLM VRAM Calculator

Calculate VRAM requirements for any LLM including model weights, KV cache, and overhead.

📊 Model Specifications

📈 VRAM Breakdown

Model Weights1.3 GB
KV Cache (1× batch)0.7 GB
Overhead (~10%)0.1 GB
Total VRAM Required2.2 GB
H100 GPUs Needed (80 GB)1

💡 Tips

  • Use 4-bit quantization to reduce VRAM by 75%
  • KV cache grows linearly with context length and batch size
  • For inference, 4-bit is usually sufficient
  • For training, 16-bit or 32-bit is recommended

📐 Formula

VRAM = weights + KV cache + overhead
Weights = P × Q / 8
KV cache = B × L × P × 2 × Q / 8
Where: P=parameters, Q=bits, B=batch, L=context

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