AI-Native Control Plane for Heterogeneous Data Centers
From rack validation to autonomous cluster operations.
XPerf turns idle GPU capital into revenue and prevents downtime costs.
At stake per 32-GPU B200 rack
Faster rack validation
Of training interruptions are GPU/memory failures
TAM — 29% CAGR
Ops team efficiency gain
GPU uptime target
Understanding the critical role of GPU utilization in AI data centers.
Data centers worldwide are facing a severe GPU utilization crisis. Despite billions invested in advanced AI infrastructure, 60-70% of GPU capacity is routinely wasted, leaving real utilization at only 30-40% across most facilities.
This massive inefficiency is driven by fragmented workload patterns, rigid scheduling, suboptimal orchestration across heterogeneous clusters, and the lack of intelligent monitoring systems that can adapt to real-time workload dynamics.
As AI inference workloads scale exponentially, the growing complexity of managing large, irregular, and highly volatile workloads has made this inefficiency increasingly unsustainable for organizations of every size.
The industry needs intelligent, AI-native orchestration that adapts in real-time to dynamic workload patterns across heterogeneous GPU infrastructure.
Operational only 80% of the time; only 35–45% of the GPU’s compute performance is used. Fragmented workloads, rigid scheduling, and suboptimal orchestration across heterogeneous clusters all compound the inefficiency.
A mix of multi-vendor, multi-generation GPUs / ASICs and Network Fabrics creates operational nightmares. Managing heterogeneous infrastructure requires intelligent monitoring that adapts to real-time workload dynamics.
GPUs, ASICs, and Network Fabrics fail frequently, causing interruptions and costly downtime. Without predictive diagnosis and automated failover, organizations face cascading performance degradation.
Pre-production rack/cluster validation in hours, not weeks. Ships with tokens/sec report and prescribed remediation.
Faster Validation
Autonomous failure prevention, self-healing, and incident knowledge base. Fully on-premise, nothing leaves your facility.
Ops Efficiency Gain
Intelligent inference + post-training scheduler across GPU and ASIC clusters. Performance-based pricing.
Revenue Boost
Coming October 2026
