Estimate transformer model parameters and GPU memory requirements. Calculate weights for attention, FFN, embeddings, and plan GPU infrastructure for training or inference.
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Running large language models requires understanding their memory footprint. Our Model Size Calculator helps you estimate parameters and GPU memory requirements for transformers, whether you're training a custom model or deploying for inference. Based on EleutherAI's Transformer Math and Kipply's parameter counting formulas.
Transformer models consist of attention layers, feed-forward networks, and embeddings. The classic formula P ≈ 12Ld² estimates parameters from layers (L) and hidden dimension (d). Memory requirements depend on precision (FP32/FP16/INT8) and whether you're training (requires optimizer states and gradients) or running inference (requires KV cache).
Parameter Formula
P = 12 × L × d_model² + V × d_modelDetermine if your model fits on a single GPU or requires multi-GPU setups with tensor/pipeline parallelism.
GPU memory requirements directly impact cloud compute costs. Right-size your infrastructure to avoid overspending.
When designing custom models, understand the parameter/memory tradeoffs of different layer configurations.
See how INT8 or INT4 quantization reduces memory requirements, enabling larger models on consumer GPUs.
Training requires: 1) Model weights, 2) Optimizer states (AdamW stores momentum and variance = 8 bytes/param), 3) Gradients (4 bytes/param), 4) Activations for backpropagation. Rule of thumb: training needs ~16-20 bytes per parameter in mixed precision, while inference needs only 2 bytes per parameter in FP16.