According to Andrej Karpathy (@karpathy) on Twitter, nanochat is a newly released open-source project that provides a minimal, from-scratch, full-stack training and inference pipeline for building a ChatGPT-like large language model (LLM). Unlike Karpathy's previous nanoGPT, which only handled pretraining, nanochat enables users to train a transformer-based LLM from pretraining through supervised fine-tuning (SFT) and reinforcement learning (RL), all in a single, dependency-minimal codebase. The pipeline includes a Rust-based tokenizer, training on FineWeb data, midtraining with SmolTalk conversations, and evaluation across benchmarks such as ARC-Easy, MMLU, GSM8K, and HumanEval. Notably, users can deploy and interact with their own LLM via a web UI or CLI after as little as four hours of training on a cloud GPU, making advanced LLM development more accessible and affordable for researchers and developers. This release lowers the entry barrier for custom LLM experimentation, offering business opportunities in rapid prototyping, education, and research tools within the AI industry (source: @karpathy).

According to Andrej Karpathy, the Llama 1B AI model can now perform batch-one inference using a single CUDA kernel, eliminating the synchronization boundaries that previously arose from sequential multi-kernel execution (source: @karpathy, Twitter, May 27, 2025). This approach allows optimal orchestration of compute and memory resources, significantly improving AI inference efficiency and reducing latency. For AI businesses and developers, this technical advancement means faster deployment of large language models on GPU hardware, lowering operational costs and enabling real-time AI applications. Industry leaders can leverage this progress to optimize their AI pipelines, drive competitive performance, and unlock new use cases in edge and cloud AI deployments.