Training GPT-3 with nanoGPT Limitations | Generated by AI

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Question: Can we train GPT-3 scale models (6.7B) using nanoGPT?

Answer:

Short Answer

nanoGPT as-is: No for 6.7B. It’s designed for GPT-2 scale (up to ~1.5B comfortably). But it’s a great starting point to extend.

What nanoGPT Supports Out of the Box

# nanoGPT max practical config (Karpathy's own runs)
n_layer = 36
n_head = 20  
n_embd = 1280  # GPT-2 XL
# ~1.5B params — that's the ceiling Karpathy targets

What’s Missing for 6.7B

Feature nanoGPT Needed for 6.7B
Model parallelism ❌ optional (fits MI300X)
Gradient checkpointing ❌ ✅ critical
Mixed precision (bf16) ✅ ✅
Gradient accumulation ✅ ✅
Flash attention ✅ ✅
Distributed (DDP) ✅ single node ✅
Activation checkpointing ❌ ✅

The Real Blocker — Optimizer Memory

6.7B model on nanoGPT default (fp32 AdamW):
  weights:   26.8GB
  grads:     26.8GB  
  m + v:     53.6GB
  activations (batch=4, seq=2048): ~30GB
  ─────────────────────────────────────────
  total:     ~137GB  ← tight but fits MI300X 192GB

With gradient checkpointing:
  activations: ~5GB  (recompute instead of store)
  total:       ~112GB ✅ comfortable

Option 1: Extend nanoGPT Yourself (Karpathy-style)

Add gradient checkpointing — literally 3 lines:

# In model.py, inside Block.forward()
from torch.utils.checkpoint import checkpoint

class GPT(nn.Module):
    def forward(self, idx, targets=None):
        ...
        for block in self.transformer.h:
            # replace: x = block(x)
            x = checkpoint(block, x)  # ← recompute activations
        ...

And bump the config:

# config/train_gpt3_6b.py
n_layer = 32
n_head  = 32
n_embd  = 4096
block_size = 2048
batch_size = 4
gradient_accumulation_steps = 160  # effective batch = 640 sequences

learning_rate = 1e-4
max_iters = 600000
lr_decay_iters = 600000
warmup_iters = 2000

# bf16 on MI300X ROCm
dtype = 'bfloat16'

out_dir = '/mnt/data/checkpoints'
dataset = '/mnt/data/tokens'

Honestly for 6.7B, step up to:

nanoGPT        → learning tool, GPT-2 scale
LLM.c          → Karpathy's newer, faster, C/CUDA, good to 1.5B
GPT-NeoX       → EleutherAI, designed for exactly this scale
litgpt         → Lightning AI, nanoGPT-style but production-ready
torchtitan     → Meta, PyTorch-native, scales to 70B+

litgpt is the closest to nanoGPT in spirit:

pip install litgpt

# config is just a yaml, same vibe as nanoGPT
litgpt pretrain gpt3-6.7b \
  --data /mnt/data/tokens \
  --out_dir /mnt/data/checkpoints \
  --devices 1 \
  --precision bf16-true

My Recommendation for You

Given you’ve already trained GPT-2 on H100/H200 with nanoGPT:

Step 1: Train GPT-2 XL (1.5B) with nanoGPT  ← you've done this
Step 2: Add gradient checkpointing to nanoGPT, push to 2.7B  ← next
Step 3: Move to litgpt or GPT-NeoX for 6.7B  ← serious run

Don’t try to force nanoGPT to 6.7B — you’ll spend more time fighting the framework than learning. nanoGPT’s value is readability and hackability at GPT-2 scale. Past that, use tools built for the job.

Bottom Line

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