Cut Model Deployment Costs While Keeping Performance With GPU Memory Swap

Cut Model Deployment Costs While Keeping Performance With GPU Memory Swap

Cut Model Deployment Costs While Keeping Performance With GPU Memory Swap | NVIDIA Technical Blog

“Deploying large language models (LLMs) at scale presents a dual challenge: ensuring fast responsiveness during high demand, while managing the costs of GPUs. Organizations often face a trade-off between provisioning additional GPUs for peak demand or risking service level agreement during spikes in traffic, where they decide between:

1. Deploying many replicas with GPUs to handle ‌worst-case traffic scenarios, paying for hardware that spends most of its time idling.
2. Scaling up aggressively from zero, with users suffering through latency spikes.

Neither approach is ideal. The first drains your budget—the second risks frustrating your users.

NVIDIA Run:ai GPU memory swap, also known as model hot-swapping, is a new innovation designed to push the boundaries of GPU utilization for inference workloads by addressing GPU memory constraints and enhancing auto-scaling efficiency…”

Source: developer.nvidia.com/blog/cut-model-deployment-costs-while-keeping-performance-with-gpu-memory-swap/