H200 GPU 20s Iteration Breakdown | Generated by AI
Question: Why does one training iteration take 20 seconds on a DigitalOcean H200 GPU?
Answer: Twenty seconds per iteration might feel slow, but it is actually expected and incredibly fast for the sheer volume of work the GPU is processing.
Here is the breakdown of why it takes ~21 seconds:
1. You are doing 64 micro-steps per “iteration”
In your config, gradient_accumulation_steps = 64. An “iteration” in nanoGPT is only logged after all 64 accumulation steps are completely finished.
- Time per micro-step: 21 seconds ÷ 64 steps = ~0.32 seconds per forward/backward pass.
- Processing a batch of 16 sequences (16,384 tokens) through a massive 1.5 Billion parameter model in 0.32 seconds is top-tier performance.
2. The math proves the H200 is crushing it
- Tokens per iteration: 16 (batch) × 1024 (block) × 64 (accumulation) = 1,048,576 tokens.
- Throughput: 1,048,576 tokens ÷ 21 seconds = ~49,900 tokens per second.
- Pushing ~50k tokens per second through a 1.5B model means the H200 is fully saturated and highly efficient. For comparison, an older A100 GPU would likely take over a minute for this exact same iteration.
3. The 164% MFU Mystery
Seeing a Model FLOPs Utilization (MFU) of 164% looks like an error, but it is actually a hardcoded artifact in the code. The nanoGPT codebase calculates MFU based on the peak performance of an A100 GPU (312 TFLOPs). Because your H200 is vastly more powerful, nanoGPT calculates the math and reports >100% relative to what an A100 could achieve.
How to adjust the pacing
If you want the console to update more frequently (so it doesn’t look frozen for 20 seconds at a time), you can lower the gradient_accumulation_steps.
For example, if you change gradient_accumulation_steps = 16, an iteration will only take ~5 seconds. However, note that doing this drops your effective batch size to ~262k tokens, meaning you would need to increase max_iters to 40,000 to still cover the full 10B dataset. Your current 21-second setup is perfectly optimal for GPU utilization.