Easy AI Tutorial: Understanding Batch Size in Model Training

Forum topic · 小凯 · 2026-03-27

Summary

This tutorial from the Easy AI series explains batch size in deep learning: the number of samples used per parameter update during training. It uses a homework analogy (large batches are fast but coarse; small batches are slow but fine-grained) and highlights that the effective batch size equals per-device batch size multiplied by gradient accumulation steps. Large batches offer more stable training, better gradient estimates, reduced risk of local optima, and higher GPU utilization, while small batches save GPU memory, capture data details, introduce helpful gradient noise, and often improve generalization. The article introduces gradient accumulation as an installment-payment technique: run multiple forward/backward passes with small batches, accumulate gradients, then update once, achieving large-batch training effects with limited memory. Recommendations include starting with batch size 1-2 for small models or datasets, using gradient accumulation to raise effective batch size, pairing larger batches with larger learning rates, and tuning flexibly based on hardware constraints.

Batch Size Explained (Easy AI Tutorial Series)

1. What Is Batch Size?

1.1 Core Concept

Batch size is the number of samples used each time model parameters are updated during training.

1.2 Intuitive Analogy

Like reviewing homework:

Large batch = doing many problems at once (fast but coarse)
Small batch = doing one problem at a time (slow but precise)

1.3 Two Key Parameters

Effective batch size = per-device batch size × gradient accumulation steps

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2. Large vs. Small Batch Sizes

2.1 Advantages of Large Batches

| Advantage | Description | |-----------|-------------| | 🎯 More stable training | More accurate gradient estimates, smoother convergence | | 🏆 Easier convergence to global optimum | Reduces the risk of getting stuck in local optima | | ⚡ Higher computational efficiency | Fully utilizes GPU parallel computing |

2.2 Advantages of Small Batches

| Advantage | Description | |-----------|-------------| | 💾 Saves GPU memory | Suitable for memory-constrained scenarios | | 🔍 Captures data details | Gradient noise helps escape local optima | | 🌟 Stronger generalization | Reduces overfitting risk |

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3. Gradient Accumulation

3.1 What Is Gradient Accumulation?

Gradient accumulation is an "installment payment" technique:

Use a small per-device batch size with multiple forward and backward passes
Accumulate gradients from several small batches
Update model parameters once at the end

3.2 The "Installment Payment" Analogy

Traditional approach (direct large batch):

💰 Pay 32 yuan at once
💾 Requires a lot of cash (GPU memory)

Gradient accumulation (installments):

💸 Pay in 4 installments of 8 yuan each
💾 Only a small amount of cash needed (GPU memory)
✅ Same total amount paid in the end

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4. Parameter Setting Recommendations

4.1 Small Models / Small Datasets

Start with batch_size = 1 or 2
Use gradient accumulation to increase the effective batch size
Pair large batch sizes with large learning rates

4.2 GPU Memory Optimization Tips

Gradient accumulation achieves the "installment payment" effect
Balance memory usage and training quality
Adjust flexibly according to hardware conditions

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Source: Easy AI Tutorial series