Dynamic GPU Resource Scaling for ML Competitions

The platform now supports dynamic scaling of GPU resources, allowing additional GPUs to be added mid-competition without interrupting ongoing battles. This is achieved by independently serving models on each GPU with vLLM, using Caddy for load balancing and health checks, and a dynamic concurrency limiter that resizes based on available resources. This enables efficient cost management and ensures competition integrity even with fluctuating demand. This post highlights the increasing demand for serving large open-weight models like GLM 5.2, emphasizing the infrastructure challenges and the need for efficient serving solutions to keep up with the capability gap closing between open and closed-weight models.