PantheonGPU
GPU health and performance validation for AI infrastructure
PantheonGPU actively tests compute, memory, interconnect, thermals, stability, and AI workloads to identify underperforming, unstable, or misconfigured GPUs across NVIDIA CUDA and AMD ROCm systems.
Member of NVIDIA Inception
Know whether your GPU is actually healthy, not just online
Normal temperatures and utilization do not prove that a GPU is performing correctly. A system can look healthy in telemetry while it underperforms, becomes unstable, or exposes a configuration problem under a specific workload. PantheonGPU exercises the hardware directly, then records what happened.
Acceptance
New GPU / Node Acceptance Testing
Validate a GPU server before placing it into production. Run focused tests after installation, repair, or delivery and keep a report with the node.
Fleet operations
Fleet Outlier Detection
Identify GPUs that behave differently from otherwise identical devices in a node or fleet, including unexpected performance, thermal, memory, and interconnect behavior.
Change control
Performance Regression Testing
Detect changes after driver, CUDA or ROCm, firmware, operating system, container, or software updates before they affect production work.
Coverage for the parts that matter
- 45+ targeted workloads for compute, memory, cache, interconnect, thermals, and stability
- NVIDIA CUDA and AMD ROCm support
- AI and LLM inference workloads, including decode, prefill, attention, cache, and serving tests
- GPU memory and cache testing
- PCIe and multi-GPU interconnect testing
- Local JSON, CSV, HTML, and trace reports
- A public performance database for comparing systems