Understanding Batch Size and Block Size

Understanding Batch Size and Block Size | Generated by AI

Home 2026.04

Question: In nanoGPT, does a batch size of 64 mean training on overlapping token windows like “I like my dog”, “like my dog. It”, etc., assuming block size is 4?

Answer:

Not exactly. The key misunderstanding is mixing up batch size with sequence windowing (block size).

1. What “block size = 4” means in nanoGPT

A block size of 4 means the model is trained on fixed-length token sequences of 4 tokens, like:

If your text is:

I like my dog. It is cute

Tokenized and chunked into block size 4 sequences, you could get examples like:

Input: [I, like, my, dog] → predict like, my, dog, .
Input: [like, my, dog, .] → predict my, dog, ., It
Input: [my, dog, ., It] → predict dog, ., It, is

So yes — there are overlapping sliding windows, but that overlap comes from how the dataset is streamed and sampled, not from batch size.

2. What batch size = 64 actually means

Batch size = 64 means:

At each training step, the model processes 64 independent sequences in parallel

Each of those 64 sequences is a separate random slice of length block_size (e.g., 4, 256, 1024, etc.).

So instead of:

one sequence: [I like my dog]

You have:

64 sequences in parallel:
  [random 4-token chunk 1]
  [random 4-token chunk 2]
  ...
  [random 4-token chunk 64]

These sequences are usually:

randomly sampled from the dataset
not sequentially dependent in the same batch
not explicitly sliding like a deterministic window in batch construction

3. Where your “sliding window” idea actually comes from

Your intuition:

“I like my dog” → “like my dog It” → “my dog It is”

This does happen, but it is due to:

dataset construction strategy (random contiguous sampling from a long token stream)
NOT batch size

In nanoGPT, this is implemented roughly like:

data = [all tokens concatenated]
x = data[i : i + block_size]
y = data[i+1 : i + block_size + 1]

where i is randomly chosen each time.

So training is effectively:

stochastic sliding window sampling
not systematic sliding across batches

4. Mental model that is correct

Think of nanoGPT training like this:

block size = width of each training example
batch size = how many examples processed in parallel per step
random sampling = where each example comes from

So:

Concept	Meaning
block size	length of each input sequence
batch size	number of sequences per step
overlap	comes from dataset sampling, not batching

5. Important correction to your example

Your assumption:

batch size 64 means sequences like shifting windows

❌ Not correct.

Correct view:

✔ Each batch contains 64 independent random 4-token slices, not a sequential sliding chain inside the batch.

If you want, I can also draw how get_batch() in nanoGPT actually samples indices — that makes it 100% concrete.

Back Donate