## System
- GPU: NVIDIA RTX PRO 6000 Blackwell Workstation Edition (96 GB)
- Driver: 595.84 (also reproduced on 580.126.09 with different signature, see below)
- OS: Ubuntu, kernel 6.17.0-35-generic (also reproduced on 6.8.1-1042-realtime PREEMPT_RT)
- CUDA 13.x, PyTorch (cu130 wheels), persistence mode ON
## Symptom
Under sustained fp32 batched-GEMM-heavy compute (PyTorch, `cublasSgemmStridedBatched`-dominated,
batch ~4096 candidates x ~20k grid points, einsums of 3x3 matrices, continuous ~100% utilization),
the GSP crashes after minutes-to-hours. The application receives
`CUDA error: CUBLAS_STATUS_EXECUTION_FAILED`; afterwards `nvidia-smi` shows `ERR!`/`[N/A]`
fields and `torch.cuda.is_available()` returns False until reboot.
## Key evidence: deterministic GSP kernel panic (Xid 120)
Every crash on 595.84 produces an identical GSP-CrashCat report — same pc, same condition,
same aux, same scause/stval, across independent runs and reboots:
NVRM: Xid (PCI:0000:01:00): 120, GSP kernel panic: condition failed (11)
@ pc:0xffffffff9300dae6, aux:0xe2, partition:4#0
Reported by libos partition:4#5 kernel v3.1
sepc:0xffffffff9300299a stval:0xffffffff70000d08 scause:0xf (store/AMO page fault)
GSP bin buildId: 6332050fe14352839619385982f83700835ef570
A physically degrading part would fail at varying locations; a fixed panic PC across
many independent crashes points to a firmware code-path defect.
## What we ruled out experimentally
- Kernel: crashes on both PREEMPT_RT 6.8 and generic 6.17.
- Driver: 580.126.09 fails differently (Xid 62 → Xid 45 → repeated Xid 119 GSP RPC
timeout → Xid 154 reset required); 595.84 fails with the Xid 120 panic above.
- Power limit: capping 600W → 400W (`nvidia-smi -pl 400`) extends time-to-crash
(~7 min → ~35 min) but does not eliminate it — consistent with a clock/power state
machine trigger, not with insufficient PSU capacity.
- No NVML queries during load; no process kills; persistence ON; reduced batch sizes;
ECC log clean; ambient temperature normal (GPU ~75 C at crash, within spec).
- The same workload ran 12.5 h continuously overnight without incident (same code/driver),
so trigger frequency is workload/state dependent, not time-deterministic.
## Repro
PyTorch loop evaluating batched 3x3 einsums + elementwise kinematics over
(4096 x ~20000) fp32 tensors, ~100% SM utilization, single process.
Open-source script and exact seeds available on request; time-to-crash varies from
minutes to hours depending on power limit / workload shape.
nvidia-bug-report.log.gz attached (captured immediately after a crash, before reboot).
