nanochat LLM Training

Skill by ara.so — Daily 2026 Skills collection.

nanochat is Karpathy's minimal, hackable harness for training LLMs end-to-end on a single GPU node. It covers tokenization, pretraining, SFT finetuning, RL, evaluation (DCLM CORE score), inference with KV cache, and a ChatGPT-like web UI. A single complexity dial (--depth) auto-configures all other hyperparameters (width, heads, LR, training horizon, weight decay) for compute-optimal training. You can reproduce GPT-2 capability (~$43,000 in 2019) for ~$48 on an 8×H100 node (~2 hours).

Installation

nanochat uses uv for dependency management:

git clone https://github.com/karpathy/nanochat.git
cd nanochat
# Install uv if needed
curl -LsSf https://astral.sh/uv/install.sh | sh
# Create venv and install deps
uv sync
source .venv/bin/activate

Key Commands

Full GPT-2 Speedrun (8×H100 node, ~2–3 hours, ~$48)

# Run the reference pipeline: data download, pretraining, SFT, eval, chat
bash runs/speedrun.sh

Pretraining (distributed)

OMP_NUM_THREADS=1 torchrun --standalone --nproc_per_node=8 -m scripts.base_train -- \
    --depth=26 \
    --run="d26_run" \
    --model-tag="d26"

Pretraining (single GPU)

python -m scripts.base_train -- \
    --depth=26 \
    --run="d26_single"

Quick Research Iteration (~5 min, GPT-1 scale)

OMP_NUM_THREADS=1 torchrun --standalone --nproc_per_node=8 -m scripts.base_train -- \
    --depth=12 \
    --run="d12_exp" \
    --model-tag="d12" \
    --core-metric-every=999999 \
    --sample-every=-1 \
    --save-every=-1

CPU / Apple Silicon (tiny model, ~minutes)

bash runs/runcpu.sh

Serve Chat UI

# After training completes
source .venv/bin/activate
python -m scripts.chat_web
# Visit http://<your-server-ip>:8000/

CLI Chat

python -m scripts.chat_cli -p "hello"

Scaling Laws / Miniseries

bash runs/scaling_laws.sh   # sweep depths for scaling law data
bash runs/miniseries.sh     # train full compute-optimal miniseries

The Depth Dial

The single most important parameter. Everything else is derived automatically:

# Smaller/faster experiments
python -m scripts.base_train -- --depth=12 --run="quick_test"

# Full GPT-2 grade
torchrun --standalone --nproc_per_node=8 -m scripts.base_train -- --depth=26 --run="gpt2_repro"

Precision / dtype Configuration

nanochat uses explicit dtype management via COMPUTE_DTYPE in nanochat/common.py. No torch.amp.autocast.

# Force fp32 for inference
NANOCHAT_DTYPE=float32 python -m scripts.chat_cli -p "hello"

# Force bf16 for training
NANOCHAT_DTYPE=bfloat16 torchrun --nproc_per_node=8 -m scripts.base_train

# float16 training (enables GradScaler automatically)
NANOCHAT_DTYPE=float16 torchrun --nproc_per_node=8 -m scripts.base_train

How it works: Weights stored in fp32 (optimizer precision), custom Linear casts to COMPUTE_DTYPE in forward pass, embeddings stored directly in COMPUTE_DTYPE to save memory.

Key Python Modules

nanochat/
├── gpt.py              # GPT nn.Module Transformer
├── engine.py           # Inference with KV Cache
├── dataloader.py       # Tokenizing Distributed Data Loader
├── dataset.py          # Download/read utils for pretraining data
├── optim.py            # AdamW + Muon optimizer (1GPU and distributed)
├── core_eval.py        # DCLM CORE score evaluation
├── loss_eval.py        # Bits-per-byte evaluation
├── checkpoint_manager.py  # Save/Load checkpoints
├── common.py           # Utilities, COMPUTE_DTYPE
├── execution.py        # Python code execution tool for LLM
└── engine.py           # Efficient KV-cache inference

scripts/
├── base_train.py       # Pretraining entry point
├── chat_web.py         # Web chat UI server
└── chat_cli.py         # CLI chat interface

runs/
├── speedrun.sh         # Reference full pipeline (GPT-2 speedrun)
├── scaling_laws.sh     # Scaling law sweeps
├── miniseries.sh       # Full compute-optimal miniseries
└── runcpu.sh           # CPU/MPS example

Real Code Examples

Load and Run Inference on a Trained Model

import torch
from nanochat.gpt import GPT
from nanochat.engine import InferenceEngine
from nanochat.checkpoint_manager import CheckpointManager

# Load checkpoint
ckpt_manager = CheckpointManager("checkpoints/d26")
model, config = ckpt_manager.load()
model.eval()

# Run inference with KV cache
engine = InferenceEngine(model)
output = engine.generate(
    prompt="Once upon a time",
    max_new_tokens=200,
    temperature=0.8,
    top_p=0.95,
)
print(output)

Custom Training Script with Depth Dial

import subprocess

def train_model(depth: int, run_name: str, nproc: int = 8):
    """Launch a compute-optimal training run for given depth."""
    cmd = [
        "torchrun",
        "--standalone",
        f"--nproc_per_node={nproc}",
        "-m", "scripts.base_train",
        "--",
        f"--depth={depth}",
        f"--run={run_name}",
        f"--model-tag={run_name}",
    ]
    subprocess.run(cmd, env={"OMP_NUM_THREADS": "1", **__import__("os").environ})

# Quick research iteration
train_model(depth=12, run_name="my_experiment_d12")

# Full GPT-2 grade
train_model(depth=26, run_name="my_gpt2_repro")

Adjust Device Batch Size for Lower VRAM

# Default device_batch_size=32 needs ~80GB VRAM per GPU
# Reduce for smaller GPUs (gradient accumulation handles the rest)
torchrun --standalone --nproc_per_node=4 -m scripts.base_train -- \
    --depth=12 \
    --device_batch_size=16 \
    --run="low_vram_run"

# Even smaller
python -m scripts.base_train -- \
    --depth=8 \
    --device_batch_size=4 \
    --run="single_gpu_small"

Monitoring Key Metrics in wandb

# nanochat logs to wandb automatically. Key metrics to watch:
# - val_bpb: validation loss in bits-per-byte (vocab-size-invariant)
#   as a function of step, total_training_time, total_training_flops
# - core_metric: DCLM CORE score (target > 0.2565 to beat GPT-2)
# - train/mfu: Model FLOPS utilization
# - train/tok_per_sec: Training throughput

# Set wandb project via env var before training
import os
os.environ