Training Time Estimate for nanoGPT

Training Time Estimate for nanoGPT | Generated by AI

Home 2026.04

Question: Given this nanoGPT training config (max_iters=12000, batch_size=8, gradient_accumulation_steps=64, RTX 4070), how long will the full training take and when will it finish from the current point at iter 1800?

Answer:

📋 Config Analysis

Tokens Per Iteration (Confirmed)

tokens_per_iter = batch_size × block_size × gradient_accumulation_steps
               = 8 × 1024 × 64
               = 524,288 tokens/iter  ✅ matches your log

Total Tokens That Will Be Seen

total_tokens = 524,288 × 12,000 = ~6.29 Billion tokens

⏱️ Precise Time Calculation

From your log:

Normal iter time: ~9,330 ms = 9.33s
Eval iter time (every 500 steps): ~29,164 ms = 29.16s
Current position: iter 1,800

Step Breakdown

Type	Count	Time Each	Subtotal
Normal iters (remaining)	10,200	9.33s	95,166s
Eval/checkpoint iters	21 (at steps 2000,2500…12000)	29.16s	612s
Total remaining			~95,778s

95,778 seconds ÷ 3600 = ~26.6 hours remaining

📐 Full Run Summary

Phase	Time
Already elapsed (iter 0→1800)	~4.7 hours
Remaining (iter 1800→12000)	~26.6 hours
Total full run	~31–32 hours

Starting now (April 29, 2026) → estimated finish: ~May 1, 2026 around 03:00–06:00 AM depending on system load, thermal throttling, and disk I/O during checkpointing.

🔍 RTX 4070 Bottleneck Analysis

Your MFU of 15.41% is the key limiter. Here’s why:

gradient_accumulation_steps = 64 means 64 sequential micro-batches per optimizer step — this is very high and serialized on a single GPU, reducing throughput efficiency
RTX 4070 has 12GB VRAM — your batch_size=8 is correctly constrained to fit
The 4070’s memory bandwidth (~504 GB/s) vs 4090 (~1008 GB/s) is ~50% slower, directly explaining the slower iter time

⚠️ Loss Convergence Reality Check

Step	Val Loss
0	10.9913
500	5.5082
1000	4.3908
1500	4.0096
12000 (projected)	~3.4–3.7

The loss is still dropping healthily. At 12,000 steps with ~6.3B tokens seen, you should reach a val loss of roughly 3.4–3.7 on standard nanoGPT — not as low as the optimized speedrun builds, but solid for a stock config on a single RTX 4070.

💡 Tips to Save Time (Optional)

If 32 hours feels too long, you can reduce without much quality loss:

# Option A: Reduce max_iters
max_iters = 8000        # saves ~9 hours, still sees ~4.2B tokens

# Option B: Reduce gradient_accumulation_steps
gradient_accumulation_steps = 32   # halves accumulation overhead
batch_size = 8                     # keep same, tokens/iter drops to 262k

# Option C: Both — fastest path to ~3.5 loss
max_iters = 8000
gradient_accumulation_steps = 32

Bottom line: at your current config, expect ~27 more hours, finishing around May 1st.

Back Donate