- GPU cold starts for AI models in production can cause long startup times, affecting scaling and resource utilization.
- Cerebrium reduces cold starts by over 80% using CPU and GPU memory snapshots to restore fully initialized containers in seconds.
- Checkpointing involves pausing execution, dumping CPU/GPU memory to files, uploading to storage, and restoring on demand.
- High-level architecture includes a checkpoint service and modified gVisor containerd shim to decide between normal boot or restore.
- Restoration times are fast, e.g., 2.25 seconds from S3 for a 9GB container, compared to 50 seconds for a full cold start.
- Real-world challenges include handling network state, multiprocessing, local runtime files, and ensuring compatibility across environments.
- Benchmarks show Cerebrium reduces cold starts by an average of 71% compared to without snapshots, and is faster than competitors like Baseten and Modal.
- Checkpointing is ideal for workloads with deterministic initialization like framework imports, model loading, and CUDA graph capture.