Start and Restore GPU Workloads in Seconds with NVIDIA Snapshot | NVIDIA Technical Blog - NVIDIA Developer

NVIDIA's sub-second GPU snapshot is not an incremental optimization—it is a generational shift in compute scheduling, analogous to Docker's 2014 disruption of KVM. Structurally, second-level checkpoint/restore rewrites three layers: the driver must expose fine-grained HBM state interfaces, memory controllers need atomic snapshot writes, and orchestration stacks gain true hot-migration. Serverless GPU inference crosses from whitepaper to production, with P99 latency finally sub-100ms. Competitively, AMD's ROCm and Intel's oneAPI lack equivalent snapshot primitives—this is an ecosystem chasm no patch can bridge. Hyperscaler silicon (Trainium, TPU) paradoxically accelerates, as snapshot logic couples tightly with proprietary schedulers. On risk, CUDA lock-in deepens exponentially; under export controls, compute elasticity itself becomes a strategic asset, and second-level recovery will be re-priced in compliance and supply-chain security audits. 12-24 month trajectory: cluster utilization jumps from 30-40% to 70%+, inference TCO drops 40-60%. The deeper long-tail effect: pretrained-weights-as-a-service emerges as a business model. Second-level checkpoint loading replaces resident deployment—compute shifts from fixed capital to a streaming commodity.