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: AWS, Azure, GCP, or any provider, 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 bucket, 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.
• Spot GPU training that actually finishes
  Spot and interruptible GPUs are the cheapest compute there is, because the provider can take them back at any moment.

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.
• VaultLayer vs Google Vertex AI
  Vertex AI is Google Cloud's managed ML platform — powerful if you're all-in on GCP and adopt its conventions.

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.
• FSDP vs DDP: which PyTorch parallelism to use
  Both are PyTorch data parallelism: every GPU sees different data, gradients are synchronized.

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.
• Guide: fix CUDA out of memory during training
  torch.cuda.OutOfMemoryError means the model, optimizer state, gradients, and activations don't fit in VRAM.
• Guide: training job keeps getting preempted
  Preemption is the price of cheap GPUs: spot and interruptible capacity can be reclaimed by the provider at any time, sometimes with seconds of notice.
• Guide: checkpoint training to S3 / object storage
  A checkpoint on the instance's local disk dies with the instance — and on spot capacity, instances die.