XPerf is an AI‑native GPU orchestration platform that provides real‑time holographic monitoring, automated fault detection, and self‑healing for heterogeneous GPU/ASIC clusters. Its AI‑driven scheduler continuously reallocates AI/ML workloads to maximize hardware utilization, reduce energy consumption, and improve stability, delivering higher throughput and lower latency for data‑center operators.
Funding
Funding not disclosed
Founders
Product
Problem
AI data centers experience chronic GPU and ASIC underutilization, with hardware operating at only 30‑40% effective capacity, leading to significant performance loss, energy waste, and frequent hardware instability. Managing heterogeneous, multi‑vendor GPU clusters and dynamic AI/ML workloads adds operational complexity and increases the risk of failures.
Solution
XPerf provides an AI‑native GPU orchestration platform that continuously monitors hardware health and workload characteristics in real time. Its holographic monitoring visualizes performance metrics, while smart diagnosis automatically detects and resolves instability issues. The platform’s AI‑driven allocation engine dynamically schedules and balances AI/ML tasks across heterogeneous GPU and ASIC resources, improving overall utilization by 20% or more. By optimizing resource distribution and reducing idle time, XPerf lowers energy consumption and enhances hardware stability, delivering higher throughput and lower latency for AI workloads.
Target Audience
Primary customers are AI data‑center operators and large‑scale machine‑learning infrastructure teams that manage heterogeneous GPU/ASIC fleets and require high utilization, energy efficiency, and hardware reliability.
Features
- Real‑time holographic dashboard that aggregates performance, temperature, and power data across multi‑vendor GPU/ASIC clusters
- Automated fault detection and self‑healing mechanisms that diagnose and remediate hardware and network fabric issues without manual intervention
- AI‑based workload scheduler that continuously reallocates tasks to match compute demand with the most suitable hardware, maximizing utilization
- Energy‑aware optimization that adjusts power states and workload placement to reduce consumption by up to 40%
- Support for heterogeneous environments, handling multiple GPU generations and network fabrics within a single orchestration layer
- API integration for existing data‑center management tools and orchestration pipelines