Serving AI models at scale is expensive, and existing solutions struggle to maintain quality of service (QoS) when handling high request volumes, leading to increased infrastructure costs. Current state-of-the-art serving systems can experience significant response time degradation when stressed with request rates exceeding their capacity. Scaling up the number of GPUs is often the only recourse, further driving up expenses.

nCompass Technologies offers a hardware-aware request scheduler and Kubernetes autoscaler designed to optimize GPU utilization for AI model serving. Their technology enables a single GPU to handle significantly more requests per second without compromising QoS, resulting in substantial cost savings. By intelligently managing requests and dynamically scaling resources, nCompass Technologies ensures low latency and high throughput for AI inference workloads. This approach allows users to achieve greater efficiency and responsiveness compared to traditional scaling methods.

Hardware-aware request scheduler that optimizes GPU utilization

Kubernetes autoscaler for dynamic resource allocation

Enables a single GPU to handle over 100 requests per second

Maintains a time-to-first-token (TTFT) of less than one second

Improves AI model responsiveness by up to 18x compared to existing solutions

Reduces the cost of serving AI models at scale by 50%

The primary customers are organizations that deploy AI models at scale and seek to reduce infrastructure costs while maintaining low latency and high throughput.