According to Andrej Karpathy, the latest Nanochat miniseries v1 demonstrates that optimizing large language models (LLMs) should focus on a family of models, adjustable via compute allocation, rather than a single fixed model. This approach leverages robust scaling laws to ensure predictable, monotonically improving results as more compute is invested, similar to findings in the Chinchilla paper (source: @karpathy, Jan 7, 2026). Karpathy's public release of Nanochat features an end-to-end LLM pipeline, showcasing experiments where model and token scaling adhered closely to theoretical expectations, with a constant relating model size to training horizons. Benchmarking the Nanochat miniseries against GPT-2 and GPT-3 using the CORE score (from the DCLM paper) provides objective validation and demonstrates the potential for cost-effective, compute-optimal model training (source: @karpathy, Jan 7, 2026). This methodology allows AI startups and enterprises to confidently budget for and deploy scalable LLMs, reducing risk and optimizing investment in AI infrastructure.