VaultLayer

VaultLayer transforms affordable, transient GPUs into dependable computing resources for intensive model training. Key features include: * Automatic job resumption after GPU failure * Seamless integration with existing training scripts * Dynamic allocation to the lowest-cost available GPUs * Zero code changes or SDKs required This platform ensures that your training tasks are completed without interruption, even when underlying hardware is reclaimed or crashes. By intelligently detecting failures and resuming from the last checkpoint, VaultLayer eliminates lost work and the need for constant monitoring. This allows engineers and researchers to leverage significant cost savings from affordable GPU providers without compromising on reliability. The system handles provisioning, secure checkpointing, failure detection, and cross-provider recovery automatically. Users simply use `vl run` to wrap their existing PyTorch or JAX scripts, and the platform manages the rest—from finding the most economical GPU to ensuring the job finishes. This approach significantly reduces average training costs and frees up valuable time. VaultLayer is ideal for engineering teams, researchers, and developers who need to fine-tune complex models economically and reliably. It allows them to focus on model development, confident that their training jobs will complete successfully, regardless of the underlying infrastructure stability.