nanochat
End-to-end, open-source recipe to train and serve a small chat LLM (~560M params) for about $100 on one 8×H100 node, with tokenizer, pretrain→midtrain→SFT→optional RL, FastAPI web UI, and a KV-cached inference engine.
At a Glance
Pricing
Open-source repository available for local use, modification, and learning.
Engagement
Available On
Updated Feb 2026
About nanochat
nanochat is an open-source, from-scratch codebase for training and serving your own small chat LLM on a tight budget. It’s designed to run a full “speedrun” on a single 8×H100 box in roughly a few hours (~$100): tokenization, base pretraining, mid-training on chat data, supervised finetuning, optional RL on GSM8K, evaluation, and a simple web UI to talk to the model.
What it includes:
- Tokenizer & data: a custom Rust BPE tokenizer and scripts to pull a shuffled subset of FineWeb-EDU for pretraining.
- Training stages: base pretraining → mid-training (SmolTalk + MMLU aux + GSM8K) → SFT; optional RL (simplified GRPO) on GSM8K.
- Evaluation: CORE / ChatCORE metrics plus task-specific scores (ARC-Easy/Challenge, MMLU, GSM8K, HumanEval), and an auto-generated
report.mdsummarizing runs. - Inference & serving: a compact engine with KV caching (prefill + decode) and a FastAPI server with a lightweight chat web UI.
- Scalability knob: model depth as the primary “slider” (e.g., d20 ≈ ~560M params), with auto-adjusted batch/accumulation.
Use it to understand the full training loop, tweak data or hyperparameters, and stand up a private, hackable chat model end-to-end.
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Pricing
Open Source
Open-source repository available for local use, modification, and learning.
- Full repository source code
- Permissive open-source usage for experimentation
- Reference implementation for an end-to-end chat LLM pipeline
Capabilities
Key Features
- End-to-end LLM training pipeline (tokenizer → pretrain → mid-train → SFT → optional RL)
- Custom Rust BPE tokenizer and data helpers
- Evaluation scripts (CORE/ChatCORE
- ARC
- MMLU
- GSM8K
- HumanEval) with auto-generated report
- KV-cached inference engine and FastAPI web UI for chat
- Single-node speedrun scripts for one 8xH100 box; depth-based scaling knob