The GPU Buildout

Fairness, noisy‑neighbor, isolation and security concerns in shared GPUs can force either dedicating GPUs per tenant or adding complex software mediation. Both approaches erode utilization and raise operational complexity, undercutting the cost advantages that justify a serverless GPU model in the first place blog.devops.dev github.com infracloud.io.

Scale‑up is limited by GPU availability and provisioning time, forcing a tradeoff between responsiveness and cost. Meeting latency targets typically requires warm pools, predictive scaling, or similar buffers—mitigations that add cost and complexity and dilute the on‑demand efficiency story blog.devops.dev github.com infracloud.io.

Core goals—fast cold starts, efficient GPU sharing/bin‑packing, and cost‑effective autoscaling—are highlighted as active areas of engineering and research, implying prolonged iteration and operational overhead before stable, defensible performance/cost characteristics can be achieved blog.devops.dev github.com infracloud.io.