What silent kernel bugs cost
In short: a silently-wrong kernel doesn’t crash — it runs at scale. The cost is
GPU-hours wasted on broken work and slow, untraceable quality regressions. PyTorch’s own triage
data shows 19.2% of high-priority torch.compile issues are silent
wrong-output bugs — the second-largest category after crashes.
GPU-hours billed for broken work
A miscompiled matmul propagates through every forward pass. Training burns tens of thousands of GPU-hours per epoch on H100/A100 — and a silently-wrong kernel means every one of those hours computes the wrong thing. The compute is still billed.
Quality regressions with no stack trace
A broken flash-attention degrades long-context quality without crashing. An unmasked reduction taints metrics no one watches. There is no error to grep for — only a model that is quietly worse, discovered weeks later if at all.
Months of green CI
Because the correctness gate is a one-shape allclose, the bug never turns CI red. Teams ship with confidence on top of a test suite that structurally cannot see the failure.
This is not hypothetical
Every modern training and inference stack now ships LLM-generated CUDA/Triton kernels — fused attention, custom matmul variants, normalisation layers. Every benchmark in the LLM-kernel literature shares the same one-shape oracle; the kernels they bless are the kernels that ship. Silent Data Corruption in numerical code is now a recognised academic sub-field for exactly this reason.
gpuemu turns that invisible failure mode into a red CI check with a replayable seed. See how the bug hides → · See the measured results →