Serverless GPU Orchestration, Snapshot Restore, and Cold-Start Mitigation in Cloud AI

Elastic artificial intelligence inference services increasingly rely on serverless execution models to eliminate idle accelerator expenditure during diurnal traffic troughs. However, provisioning graphics processing unit (GPU) containers on demand introduces severe cold-start latency penalties ranging from 18.4 to 64.2 seconds due to OCI container image decompression, Python runtime initialization, CUDA context creation, and multi-gigabyte neural network weight deserialization over network-attached storage. This paper presents an integrated serverless GPU orchestration architecture that combines lazy content-addressable chunk streaming, unified host-and-device Checkpoint/Restore In Userspace (CRIU-GPU), and locality-aware NVMe-to-VRAM direct memory access (GPUDirect Storage) to reduce 99th-percentile cold-start latencies to sub-two-second bounds. We formulate the cold-start decomposition as a constrained stochastic dynamic program that balances pre-warmed microVM pool expenditure against tail-latency service-level objective (SLO) violations under non-stationary Hawkes process request arrivals. By decoupling read-only tensor pages from mutable CUDA driver state, our snapshot restoration engine streams pinned host page frames and device memory segments concurrently across PCIe Gen5 x16 interconnects at 54.8 GB/s while bypassing redundant kernel module initialization. We examine production deployments across AWS Lambda SnapStart microVMs, the Modal content-addressable container file system, and RunPod FlashBoot serverless GPU clusters. Empirical benchmarks on 7-billion to 70-billion parameter transformer models demonstrate that combined lazy filesystem tree prefetching and unified CUDA driver checkpointing reduce median container startup latency from 28.60 seconds to 1.42 seconds and P99 cold-start latency from 49.80 seconds to 2.18 seconds. Simultaneously, fleet-wide GPU compute utilization increases from 31.4% under static over-provisioning to 78.6% under predictive snapshot orchestration, reducing monthly accelerator infrastructure cost by 56.3% without violating strict 2.5-second inference startup guarantees. Research Domain & Specialization Track Master of Science in Information Technology (MSIT) — Cloud-Native GPU Compute and Serverless Systems. Core Technical Highlights Empirical Production Case Studies: Rigorous architectural blueprints and telemetry evaluation across enterprise cloud deployments. Four-Tier Reference Architecture: Formal specification from edge ingress to distributed hardware execution pools. Mathematical Formulation: Formally derived algorithmic and optimization models with closed-loop verification. Multi-Modal Benchmarking: Comparative empirical baselines under fault-injection and synthetic workload scaling. Author Information Author: Sonu Kumar SinghRole: Senior Consultant Affiliation: Independent Researcher Publication Status: Preprint / Working Paper (MSIT AI & Cloud Systems Research Series) Reproducibility & Open Science All included architecture diagrams, telemetry schemas, and benchmark curves are open-access under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.