Cost-Effective GPU Utilization

Topic:

Implemented a time-sharing GPU scheduler that dynamically allocates GPU slices using 1 slice = 200mb ov vRAM for real-time inference demand. This optimization layer ensures GPU utilization remains above 90% during peak traffic. Creating systems like this to work with cluster autoscaling maintain balance and cost effective compute.

Why it matters:

• Achieves enterprise-grade inference throughput at startup-level cost.

• Enables continuous analysis of social data feeds without scaling compute.

• Aligns KOAT’s infrastructure with sustainable AI operations and lower carbon footprint.