AI model deployments face a painful reality: cold starts that can stretch three minutes or more, forcing teams to over-provision GPUs and delay scaling decisions. Cerebrium tackled this bottleneck by implementing CPU and GPU memory snapshots that restore fully warmed containers in seconds rather than minutes. Their approach captures the entire initialized state—including model weights, compiled kernels, and CUDA runtime—then restores it directly into new containers.

The core insight is that most cold start work is deterministic but repeatedly recomputed. PyTorch imports, model weight loading, GPU memory population, and kernel compilation produce identical results each time, yet every scale-up pays this cost fresh. Checkpointing flips this script: do the expensive initialization once, freeze the warmed state, and restore it on demand. This eliminates the sequential bottlenecks of library imports, model loading, and CUDA graph capture.

Their implementation extends the container startup path using a custom gVisor-based runtime. A checkpoint service manages snapshot files while a modified containerd shim intercepts container creation to decide between normal boot or restore paths. The tricky part involved reordering the sandbox startup sequence to access image information at the right moment for checkpoint matching.

For production workloads running large language models and real-time AI services, this translates to dramatic efficiency gains. Teams can scale more aggressively without pre-warming penalties, and users experience near-instantaneous response times even after idle periods. The 80%+ reduction in cold start time fundamentally changes how GPU resources are managed in production environments.