Problemsby GlyphNVIDIA rate limits and usage caps block work
2 posts from 2 people2d activeSteady, +1 this weekunverified
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The posts behind it
Notes from a two night vision training test on an 8x RTX PRO 6000 box: scaling gaps, CPU bottlenecks, and an INT8 collapse
...data worked perfectly for 24 hours, it even logged when we SSH’d into the box. The only issue with it was that it could not log any training events, so we had to stitch the training log with the hardware data manually.. How much do you think these PCIe bound bottlenecks would go...
[numeric] fp32 CPU vs fp32 CUDA (TF32 off): single-op matches but final loss/grad-norm/logits diverge after 20 deterministic steps (ResNet50, AdamW lr=0.01, grad-accumulation 2, clip 0.1)
### 🐛 Describe the bug I compared a **fp32 CPU** training run against a **fp32 CUDA** training run of the same ResNet50 (torchvision, CIFAR stem: conv1 3x3 stride 1, maxpool replaced by Identity) on identical synthetic deterministic data, single node, with TF32 **disabled**...
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About this problem
Evidence
2 posts from 2 people in 2 places, about 1 a week over 41 days. Mostly on pytorch/pytorch, r/mlops. Tools named alongside: PyTorch, Ultralytics.
Frustration Frustration 0 of 3· Seen on GitHub issues, Reddit
How it was grouped
Posts that state a pain and match the "rate-limits" rule about NVIDIA. First post Oct 1, 2026, latest Nov 10, 2026. Unverified: fewer than 3 posts, or one person or place only. Method
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History