Resources

Guides and comparisons for running reliable AI training and fine-tuning on your own cloud or elastic GPU capacity.

Use cases

What teams run on VaultLayer.

• BYOC GPU training — run AI training on your own cloud
  BYOC — bring your own cloud — means your training jobs run on compute you already control: your cloud account, reserved instances, or a committed GPU contract.
• Fault-tolerant training — auto-resume after interruptions
  Interruptible and spot GPUs are cheap because they can be reclaimed at any moment — and a reclaim mid-run normally wipes out hours of training progress.
• Fine-tune LLMs on your own cloud, reliably
  VaultLayer runs LLM fine-tuning jobs — LoRA, QLoRA, or full fine-tunes — on your own cloud or on elastic external GPUs, with no changes to your training code and automatic recovery if a GPU is interrupted..
• Distributed multi-node GPU training
  Multi-node training spreads a single job across several GPU machines so larger models and datasets finish in less wall-clock time.
• Run AI training on your own AWS GPUs
  VaultLayer's BYOC model runs your training jobs on your own AWS account — your GPU instances, your S3 buckets, your pricing — while adding orchestration, checkpointing, and recovery on top.
• Run AI training on your own GCP GPUs
  VaultLayer's BYOC model runs your training jobs on your own Google Cloud account — your GPU instances, your GCS buckets, your pricing — while adding orchestration, checkpointing, and recovery on top.
• Use your cloud credits for GPU training
  Funded teams often sit on cloud credits, reserved instances, or committed-use discounts that go underused — because turning that raw capacity into a reliable training platform is work.

Compare

How VaultLayer compares to the alternatives.

• VaultLayer vs SkyPilot
  SkyPilot and VaultLayer both run training jobs across multiple clouds, but they sit at different layers.
• VaultLayer vs renting GPUs directly (RunPod, Lambda, Vast.ai)
  Renting H100s straight from RunPod, Lambda, or Vast.ai is the cheapest-looking option until you count the babysitting tax: hunting for a region with capacity, paying for idle boot minutes, wiring your own checkpoint sync, and writing resume logic because a crash just hands you a fresh pod.
• Managed vs self-hosted GPU training
  Every team running GPU training picks a point on a spectrum: build and operate the infrastructure yourself, or use a managed control plane that handles reliability for you.
• VaultLayer vs AWS SageMaker
  Both run managed training jobs, but Amazon SageMaker is tied to AWS and its own SDK, while VaultLayer is cloud-agnostic and wraps the training command you already have.
• VaultLayer vs Modal
  Modal is a serverless compute platform where you express work as Python functions using its SDK and run them on Modal's infrastructure.
• VaultLayer vs CoreWeave
  CoreWeave is a specialized GPU cloud — a place to rent large fleets of GPUs.
• VaultLayer vs RunPod
  RunPod is a GPU cloud where you rent pods and clusters by the hour.
• VaultLayer vs Slurm
  Slurm is the open-source scheduler behind many on-prem GPU clusters — powerful, but you operate the cluster and write job scripts.

Frameworks

Run your framework on cloud GPUs, unchanged.

• Run PyTorch training on cloud GPUs
  VaultLayer runs your existing PyTorch training script on cloud GPUs with no code changes — single-GPU or distributed — and adds checkpointing and automatic recovery so a reclaimed or crashed GPU never costs you the run..
• Train Hugging Face models on cloud GPUs
  VaultLayer runs Hugging Face training — Transformers Trainer, TRL, Accelerate, and PEFT/QLoRA — on cloud GPUs without code changes.

Learn

The concepts behind reliable GPU training.

• What is a GPU training control plane?
  A GPU training control plane is the management layer that decides where AI training jobs run and keeps them running reliably — handling provisioning, monitoring, checkpointing, and failure recovery — independently of the GPUs that execute the work..
• What is BYOC for AI training?
  BYOC — bring your own cloud — for AI training means you run training jobs on compute you already own and pay for, while a separate control plane adds the management and reliability layer.
• How checkpoint & resume works on spot GPUs
  Spot and interruptible GPUs can be reclaimed at any moment.
• GPU types for training: H100 vs A100 vs L40S
  The right GPU for training depends on model size, precision, and budget more than on raw benchmarks.
• QLoRA vs LoRA vs full fine-tuning
  These three fine-tuning methods trade GPU memory against flexibility.
• How much GPU memory to fine-tune an LLM
  VRAM is usually the deciding factor in fine-tuning, and it swings enormously with method.

Guides

Step-by-step guides for reliable training.

• Guide: resume PyTorch training after a crash
  If training dies partway — a spot reclaim, a host crash, or an out-of-memory error — you don't want to start over.