Switch to nanochat with training adjustments | Generated by AI
Question: Should I pivot from nanoGPT to nanochat for my next training run, given the cost/time tradeoffs and training dynamics I’m observing?
Answer:
Yes, strategically this makes sense — but with caveats baked into how you approach it.
Why nanochat over nanoGPT:
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Faster convergence per token — nanochat includes improvements (likely better initialization, attention patterns, maybe optimized hyperparameters) that compress the useful learning into fewer steps. Your $29 / 14 hours on nanoGPT tells you the baseline; nanochat should juice that.
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The plateau you’re hitting is real — Early loss drops fast because the model is learning high-level statistical structure (word frequencies, basic syntax). The long tail is where real understanding lives — compositional reasoning, nuanced semantics, in-context learning. That requires scale. nanochat’s architecture may flatten that curve somewhat, but you’re right: you hit a ceiling without more data or compute.
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Cost math on AMD MI300X — At $2/hr, a month-long run is ~$1,440. That’s the cost of one serious training window. If nanochat cuts your actual training time by 20-30% (not just wall-clock, but tokens-to-convergence), you’re looking at $1,000–1,100. Acceptable for what you’re learning.
The hard truth you already know:
- Data » architecture tweaks — Once you’ve fixed obvious bugs/hyperparams, throwing more diverse, higher-quality text at it is the only lever. nanochat won’t overcome a shallow corpus.
- Compute scaling follows loss curves — With Chinchilla scaling laws, doubling compute gives you ~4% loss improvement. A month on MI300X won’t match what you’d get on a 8-GPU setup running 3 months, but it’s what you have.
- Plateau detection — Log validation loss every 1K steps. If it’s flat for 50K steps, you’re data-limited. Stop early, analyze what the model learned, iterate the dataset. Don’t burn money on a static curve.
What I’d do:
- Run nanochat for 1–2 weeks first (not a month) — Get a sense of its actual convergence vs. nanoGPT. Validate the “faster” claim with your data/hardware combo.
- Instrument training rigorously — Log perplexity, loss breakdown by category (if you can), sample generations every N steps. You need signal about what it’s learning, not just loss numbers.
- Prepare a larger/better dataset in parallel — Don’t wait until month 3 to realize your corpus was thin. Spend time now curating text (code, technical writing, conversations — whatever aligns with your AI engineering goals).
- Consider multi-task or instruction-tuning after — If you train a base model for 2 weeks, you can fine-tune it on narrower tasks (coding, reasoning, whatever) for another week on smaller compute. Often better signal than extending base training.
On the $29 sunk cost: That was $29 of experimentation value. You learned the training loop, caught bugs, validated your setup. nanochat should be faster because you already know the pipeline. Ship it.