Stealthium provides a GPU‑powered observability and security platform that captures real‑time kernel traces and low‑level telemetry from NVIDIA GPUs, converting this data into high‑level “Hyperprints” for clear insight into AI workload performance. The solution continuously monitors GPU usage to detect anomalous behavior and protect against attacks such as GPU rootkits, model poisoning, and data exfiltration, while supporting the full NVIDIA software stack and multi‑cluster environments.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises deploying AI workloads on GPUs lack visibility into low‑level GPU activity, making it difficult to detect performance regressions, security breaches, or malicious code running inside CUDA kernels. This blind spot leaves AI infrastructure vulnerable to model theft, data exposure, and undetected runtime anomalies.
Solution
Stealthium delivers a GPU‑powered observability and security platform that captures detailed kernel traces and telemetry from NVIDIA GPUs in real time. It converts raw low‑level metrics into high‑level “Hyperprints,” providing clear, actionable insights into workload performance and behavior. The platform continuously monitors GPU usage patterns to identify abnormal activity, enabling runtime protection against attacks such as GPU rootkits, model poisoning, and data exfiltration. Integration supports the full NVIDIA software stack—including drivers, CUDA, and major AI frameworks—allowing seamless deployment across on‑premise clusters and cloud environments. Security events are correlated with GPU telemetry, giving operators the context needed to mitigate threats quickly.
Target Audience
Primary customers are large enterprises, cloud service providers, and data center operators that run high‑performance AI workloads on NVIDIA GPUs and require both performance monitoring and runtime security.
Features
- Real‑time collection of GPU kernel traces and low‑level metrics with a custom monitoring layer that outperforms standard NVML in latency and data richness
- Hyperprint abstraction that translates raw telemetry into concise, high‑level visualizations of AI workload health and performance
- Runtime security engine that detects anomalous GPU usage patterns, including unauthorized memory access and privilege escalation within CUDA kernels
- Full compatibility with NVIDIA drivers, toolkits, CUDA, and leading AI frameworks for end‑to‑end observability across the AI stack
- Multi‑GPU, multi‑cluster support enabling centralized monitoring of distributed AI infrastructure
- API and dashboard for correlating security alerts with detailed GPU telemetry, facilitating rapid incident response