Transformer Layer Count Impact on Performance | Generated by AI
In NanoGPT, the number of transformer blocks (controlled by the n_layer parameter) determines the depth of the model, which directly impacts its capacity to learn hierarchical representations in language data. The standard configurations use 6 layers for the lightweight Shakespeare character-level example (to fit on a single GPU in minutes) and 12 layers to reproduce the GPT-2 124M model (on multi-GPU setups over days). Reducing this to 4 or 1 layer creates a shallower model that’s faster to train and uses less memory, but it trades off performance—typically resulting in higher validation loss, underfitting, and lower-quality text generation.
Key Effects of Fewer Layers
- Model Capacity and Performance: Each transformer block adds self-attention and feedforward layers that build increasingly abstract features (e.g., from tokens to syntax to semantics). Fewer blocks limit this stacking, so the model struggles with complex patterns. On the Shakespeare dataset:
- 6 layers (default): ~1.47 validation loss after ~3 minutes on an A100 GPU; generates coherent but imperfect Shakespeare-like text (e.g., “To be or not to be…”).
- 4 layers: ~1.88 validation loss after ~3 minutes on CPU (with scaled-down embeddings/heads for feasibility); samples are noisier and less structured (e.g., “GLEORKEN VINGHARD III: Whell’s the couse…”), showing a “hint of the right character gestalt” but more garbled output.
- 1 layer: No direct benchmarks in NanoGPT docs or common experiments, but expect even higher loss (~2.0+ based on scaling trends) and primitive generation—essentially a single attention + MLP pass, good for toy demos of basic n-gram-like prediction but failing on nuanced language modeling. It might overfit quickly to short sequences but generalize poorly.
- Training and Resource Impact:
- Speed/Memory: 4 layers cuts training time by ~20-30% vs. 6 layers on similar hardware and halves memory use (e.g., fits easily on CPU or low-end GPUs). 1 layer would be ~50-70% faster still, trainable in seconds on CPU.
- Hyperparameters: You’d often pair fewer layers with smaller
n_embd(e.g., 128-256) andn_head(e.g., 4) to avoid underutilization, plus fewer iterations (e.g., 1000-2000) since the model converges faster but plateaus earlier. - Scaling Behavior: Experiments show loss improves logarithmically with layers (e.g., 6 layers: ~1.78 val loss; 8: ~1.73; 12: ~1.68 on similar setups). Deeper models (12+ layers) excel on larger datasets like OpenWebText, hitting ~2.85 val loss for GPT-2 reproduction.
How to Experiment in NanoGPT
To test this, modify config/train_shakespeare_char.py (or your config file) and run:
python train.py config/train_shakespeare_char.py --n_layer=4 --n_head=4 --n_embd=128 --max_iters=2000
For 1 layer: --n_layer=1 --n_head=2 --n_embd=64 (further scaled to keep it lightweight). Use sample.py on the output checkpoint to generate text and compare.
In summary, 4 layers is a practical reduction for quick prototyping or low-resource runs (still usable output), while 1 layer is more educational than effective—great for understanding the block’s role but not for serious modeling. If you’re targeting GPT-2’s 12 layers, dropping to 4 would roughly double the loss and halve coherence on benchmarks.
karpathy/nanoGPT GitHub
VizuaraAI/nano-gpt-oss GitHub