Embedding Layer Size Calculator
Calculate the parameter count and memory footprint of an embedding layer.
Inputs
Parameters
38,597,376
Memory
73.62MB
Step by step
Parameters: vocab × dim
50,257 × 768
= 38,597,376
Memory: params × bytes_per_param
38,597,376 × 2 bytes
= 73.62 MB
How it works
An embedding layer is a lookup table of shape (vocabulary_size × embedding_dimension). Each token in the vocabulary has its own dense vector. For GPT-2's 50,257 vocab with 768-dim embeddings, that's 38.6M parameters taking ~73 MB in FP16.
Formula
Embedding Size
params = vocab_size × embedding_dim; memory = params × bytes_per_param
- vocab_size
- Number of unique tokens in vocabulary
- embedding_dim
- Dimensionality of each token vector
Frequently Asked Questions
Are embedding parameters trainable?
Yes — embedding vectors are learned during training and encode semantic meaning. Similar words end up with similar embedding vectors (word2vec property).
Why do LLMs have such large embedding layers?
Large vocabularies (32K-100K+ tokens) combined with high embedding dimensions (4096+) mean embedding layers can have hundreds of millions of parameters — a significant fraction of smaller models.
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