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.

According to Andrej Karpathy, the NanoChat d32 model—a depth 32 version trained for $1000—has completed training in approximately 33 hours, demonstrating significant improvements in key AI benchmarks. The model achieved a CORE score of 0.31, notably higher than GPT-2's score of 0.26, and saw GSM8K performance jump from around 8% to 20%. Metrics for pretraining, supervised fine-tuning (SFT), and reinforcement learning (RL) all showed marked increases (Source: Karpathy, Twitter; GitHub repo for NanoChat). Despite the model's low cost relative to frontier LLMs, Karpathy notes that user expectations for micro-models should be tempered, as they are limited by their size and training budget. The business opportunity lies in the rapid prototyping and deployment of small LLMs for niche applications where cost and speed are prioritized over state-of-the-art performance. Karpathy has made the model and training scripts available for reproducibility, enabling AI startups and researchers to experiment with low-budget LLM training pipelines.