GPU Memory vs Compute Utilization Explained | Generated by AI
Ah, this is a common point of confusion. The GPU memory usage being high (like 90% in your case) doesn’t mean your GPU is fully utilized. Memory usage and compute utilization are related but separate metrics. Here’s why you see it:
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Model and optimizer take a lot of memory
- Your 125M-parameter model + Adam optimizer + gradients + temporary buffers can easily take up most of the GPU memory, especially on GPUs with 12 GB like your 4070.
- This is memory allocated, not actively computing.
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Small model = low FLOPs usage
- Even if almost all memory is used, the actual compute utilization (MFU) can remain low because the operations are small. The GPU isn’t doing enough parallel math to saturate all cores.
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Memory fragmentation and caching
- PyTorch (or CUDA) often pre-allocates or caches memory to avoid repeated allocations. So the memory bar looks full even if some of it isn’t actively being used at this instant.
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Batch size impact
- Larger batch sizes increase both memory usage and compute utilization. But if your batch is small, the memory is mostly taken by static model parameters and optimizer states, not actual heavy computation.
So in your case:
- ~90% memory usage = normal for a model + optimizer on a 12 GB GPU.
- ~10% MFU = normal for a small model with small batches — the GPU is waiting on small matrix multiplies rather than crunching huge matrices.
If you want, I can break down roughly how much memory each part (model, optimizer, gradients, etc.) is taking for your 125M NanoGPT model. It’s quite instructive.