Maximize AI Infrastructure Throughput by Consolidating Underutilized GPU Workloads

In production Kubernetes environments, the difference between model requirements and GPU size creates inefficiencies. Lightweight automatic speech recognition (ASR) or text-to-speech (TTS) models may require only 10 GB of VRAM, yet occupy an entire GPU in standard Kubernetes deployments. Because the scheduler maps a model to one or more GPUs and can’t easily share across GPUs across models, expensive compute resources often remain underutilized. Solving this isn’t just about cost reduction—it’s.