NanoGPT VRAM vs Batch Size Chart | Generated by AI
Question: Can you make a detailed nanoGPT VRAM vs batch size / context size chart for RTX 4070 vs RTX 3090?
Answer:
Here’s a practical estimation chart showing how VRAM limits batch size and context length (block size) for training nanoGPT models on RTX 4070 (12GB) vs RTX 3090 (24GB). Assumptions:
- FP16 precision (half-precision) training.
- Model size: 124M parameters (~GPT-2 small).
- Overhead included for optimizer states (~2× model size in memory).
- Memory for caching, gradients, and other buffers included.
- Approximate—real numbers may vary depending on PyTorch/nanoGPT versions.
VRAM vs Batch Size / Context Length (Block Size)
| GPU | VRAM | Block Size (Seq Len) | Max Batch Size | Notes |
|---|---|---|---|---|
| RTX 4070 | 12 GB | 512 | 64 | Small context, can fit moderate batch |
| RTX 4070 | 12 GB | 1024 | 32 | Typical nanoGPT config |
| RTX 4070 | 12 GB | 2048 | 16 | Long context reduces batch drastically |
| RTX 4070 | 12 GB | 4096 | 8 | Very long context—training slow, gradient accumulation needed |
| RTX 3090 | 24 GB | 512 | 128 | Double batch compared to 4070 |
| RTX 3090 | 24 GB | 1024 | 64 | Can train efficiently without accumulation |
| RTX 3090 | 24 GB | 2048 | 32 | Long context feasible, better stability |
| RTX 3090 | 24 GB | 4096 | 16 | Very long context manageable |
Observations
- VRAM doubles effective batch size: 24GB on 3090 allows twice the batch or double the context size compared to 12GB on 4070.
- Block size impacts memory heavily: Memory scales roughly linearly with sequence length. Doubling block size halves the batch size you can fit.
- Gradient accumulation can compensate: If VRAM is limited, you can use smaller batches and accumulate gradients over multiple steps to simulate larger batches—but training is slower.
-
Small vs Large models:
- Small models (100–150M parameters) are feasible on both GPUs.
- Medium models (300M–1B) benefit from 3090’s larger VRAM, avoiding complex memory hacks.
Example Scenario:
-
Training GPT-124M on block size 1024:
- RTX 4070: max batch size 32 → need gradient accumulation if you want batch 64.
- RTX 3090: max batch size 64 → no accumulation needed, faster and simpler training.
If you want, I can make a full chart for multiple GPT model sizes (124M, 355M, 1.3B) vs VRAM and context length, which will let you plan exactly what model and batch size fits each GPU. This is the most practical for real nanoGPT training planning.
Do you want me to do that full chart?