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Michael Brenndoerfer

ALBERT › Memory and Speed Trade-offs

Michael BrenndoerferUpdated July 19, 20253 min read

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Part 24 of 67BERT and Variants

Chapter 174ALBERT › Memory and Speed Trade-offs

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Parameter sharing reduces memory for storing weights but doesn't reduce computation. ALBERT still performs the same number of matrix multiplications as BERT. The savings come from:

Model storage: The saved model is smaller.
Gradient memory: Only one set of layer gradients needed.
Optimizer states: Adam's momentum and variance buffers are smaller.

However, inference speed remains similar because the same operations execute.

In[41]:

Code

def compare_memory_requirements(bert_params, albert_params):
    """Compare memory requirements for training."""

    # Assumptions: FP32 (4 bytes), Adam optimizer (2x model for momentum/variance)
    bytes_per_param = 4

    bert_model_memory = bert_params * bytes_per_param / 1e9  # GB
    albert_model_memory = albert_params * bytes_per_param / 1e9

    # Adam optimizer states
    bert_optimizer_memory = 2 * bert_model_memory
    albert_optimizer_memory = 2 * albert_model_memory

    # Gradients (same as model)
    bert_gradient_memory = bert_model_memory
    albert_gradient_memory = albert_model_memory

    return {
        "bert": {
            "model": bert_model_memory,
            "optimizer": bert_optimizer_memory,
            "gradients": bert_gradient_memory,
            "total": bert_model_memory
            + bert_optimizer_memory
            + bert_gradient_memory,
        },
        "albert": {
            "model": albert_model_memory,
            "optimizer": albert_optimizer_memory,
            "gradients": albert_gradient_memory,
            "total": albert_model_memory
            + albert_optimizer_memory
            + albert_gradient_memory,
        },
    }

Out[42]:

Console

Training Memory Requirements (FP32, Adam optimizer):
--------------------------------------------------
Component               BERT-Base  ALBERT-Base
--------------------------------------------------
Model weights              0.44 GB       0.05 GB
Optimizer states           0.88 GB       0.10 GB
Gradients                  0.44 GB       0.05 GB
--------------------------------------------------
Total (weights only)       1.76 GB       0.19 GB

Note: Activation memory (batch, sequence length) is the same for both.

Out[43]:

Visualization

Stacked bar chart comparing memory requirements for BERT and ALBERT training components. — Training memory breakdown for BERT vs ALBERT. Parameter-related memory (weights, optimizer states, gradients) scales with model size. ALBERT's smaller parameter count dramatically reduces these components, though activation memory remains unchanged.