Compact NanoGPT Versus GPT-2 Performance | Generated by AI
Model Overview and Scale Comparison
Your nanoGPT model, with approximately 30 million parameters (6 layers, 6 heads, 384 embedding dimensions), is a compact implementation trained on the OpenWebText dataset for 20,000 iterations. This configuration is designed for resource-constrained environments, such as limited GPU memory, using a reduced context length (512 tokens) and effective batch size (64 via gradient accumulation). In contrast, the original GPT-2 models from OpenAI range from 124 million parameters (small variant, closest to your scale) up to 1.5 billion (XL variant), trained on the proprietary WebText dataset—a higher-quality precursor to OpenWebText—with much larger-scale training (e.g., billions of tokens and extensive iterations). [1][2]
NanoGPT is explicitly built to replicate GPT-2’s architecture and training dynamics on open datasets like OpenWebText, but your model’s smaller size and shorter training limit its capabilities compared to even the smallest GPT-2. Expect your model to generate shorter, less coherent text with higher repetition and factual inaccuracies, while GPT-2 (even small) handles longer contexts and more diverse outputs better. [3][3]
Performance Metrics: Perplexity and Loss
Perplexity (a measure of prediction uncertainty; lower is better) and training/validation loss are key indicators for language models like these. Your setup uses OpenWebText, an open approximation of WebText, so direct apples-to-apples comparisons are approximate but informative.
-
Your Model’s Expected Performance: With 30M parameters and 20,000 iterations (roughly covering a fraction of OpenWebText, given ~8-10 billion tokens total), expect a validation perplexity in the range of 80-120 after training. This is based on similar small nanoGPT runs: a 50M parameter model (slightly larger than yours) achieved a perplexity of ~103 after just 2 epochs on a 10GB subset of OpenWebText. Your shorter context (512 vs. GPT-2’s 1024) and fewer iterations will likely yield higher perplexity, meaning poorer next-token prediction. Training loss might plateau around 4.0-5.0, reflecting underfitting due to scale. [4]
-
GPT-2 Small (124M Parameters) Performance: On WebText, GPT-2 small reaches a validation perplexity of ~35-40, with training extending to millions of tokens over longer schedules. NanoGPT reproductions on OpenWebText achieve similar results for the 124M variant (perplexity ~35-45), but note that OpenWebText is noisier, slightly inflating scores by 5-10% compared to proprietary WebText. Larger GPT-2 variants drop to ~20-30 perplexity (e.g., XL at 35.8 on their eval set, but adjusted for scale). [3][3][5][6]
| Metric | Your 30M Model (Est.) | GPT-2 Small (124M) | GPT-2 XL (1.5B) |
|---|---|---|---|
| Parameters | 29.94M | 124M | 1.5B |
| Val Perplexity (OpenWebText/WebText equiv.) | 80-120 | 35-45 | ~20-35 |
| Context Length | 512 | 1024 | 1024 |
| Training Tokens (Approx.) | ~1-2B (20k iters @ 32k tokens/iter) | 8-40B+ | 40B+ |
| Typical Loss Plateau | 4.0-5.0 | 3.0-3.5 | 2.5-3.0 |
These estimates highlight a ~2-3x performance gap in perplexity for your model vs. GPT-2 small, scaling worse for generation quality. [4][5]
Generation Quality and Capabilities
-
Coherence and Length: Your model will produce short, repetitive outputs (e.g., basic sentences or paragraphs with looping phrases) due to its size and training brevity. GPT-2 small generates more fluid, essay-like text (up to 1,000+ tokens) with better stylistic variety, though it still hallucinates facts. Larger GPT-2 variants excel at creative writing, summarization, and zero-shot tasks. [7][5]
- Benchmark Examples:
- Text Completion: Prompt: “The future of AI is”. Your model might output: “The future of AI is in the machines that will change the world.” (basic, repetitive). GPT-2: “The future of AI is bright, with advancements in neural networks enabling unprecedented applications in healthcare, autonomous vehicles, and beyond.” (more detailed, context-aware).
- Downstream Tasks: On benchmarks like WikiText-103 or LAMBADA, GPT-2 small scores ~20-30% accuracy in cloze tasks; your model might hit 5-15%, akin to tiny models. Fine-tuning could narrow this gap for specific domains. [5]
- Limitations of Your Setup: Reduced dropout (0.0), smaller batch size, and no advanced scheduling (e.g., cosine decay beyond linear) may lead to overfitting on OpenWebText’s noise. GPT-2 benefits from cleaner data and optimizations like layer norm tweaks. For better results, extend to 50k+ iterations or scale to 124M params to match nanoGPT’s GPT-2 reproduction. [3][3][8]
Recommendations for Improvement and Fair Comparison
To benchmark your model directly against GPT-2:
- Evaluate Perplexity: After training, run nanoGPT’s
eval.pyon OpenWebText validation splits. Compare to Hugging Face’s GPT-2 small (load viatransformerslibrary) evaluated on the same data. - Scale Up: Switch to nanoGPT’s Shakespeare or default config for 124M params; it closely matches GPT-2’s loss curves on OpenWebText. [3]
- Finetuning: Start from your checkpoint and finetune on targeted data (e.g., dialogue for chat) to boost usability—GPT-2 shines here with just 354M params for conversational AI. [7]
- Hardware/Extensions: Your setup (32k tokens/iter) is efficient; on better hardware, increase
max_itersto 100k+ for perplexity under 60.
Overall, your model is a solid educational or prototyping tool but lags GPT-2 in depth due to size—think of it as a “mini-GPT-2” with 1/4 the capacity. For production, consider pretrained GPT-2 weights from Hugging Face as a baseline. [3][9]
Citations: [1] https://www.kdnuggets.com/2023/05/deep-dive-gpt-models.html [2] https://openai.com/index/better-language-models/ [3] https://github.com/karpathy/nanoGPT [4] https://www.reddit.com/r/LocalLLaMA/comments/1oslucq/whats_the_lowest_gpt2_pretraining_loss_achievable/ [5] https://www.researchgate.net/figure/Comparing-a-generated-GPT-2-model-on-OpenWebText-to-training-from-scratch-Results-show_fig2_396143265 [6] https://gitlab.au.dk/au204573/gitmal/-/blob/c660ef7dfa8447d956db7d00898536eeef29fe54/L11/NanoGPT/README.md [7] https://vatsadev.github.io/articles/nanochatgpt.html [8] https://arxiv.org/pdf/2506.00315 [9] https://medium.com/@neuralnikitha/build-your-own-chatgpt-in-an-afternoon-the-nanogpt-guide-7a0425acf4cb