Distributed multi-node GPU training, without the orchestration pain

Multi-node training spreads a single job across several GPU machines so larger models and datasets finish in less wall-clock time. The hard part isn't the math — it's bringing up a healthy cluster, wiring inter-node networking, and recovering the whole group when one node drops. VaultLayer handles that orchestration for you.

What multi-node training involves

In multi-node training, your job runs as a group of processes across several machines that exchange gradients or shards over the network (NCCL collectives in PyTorch). Every node has to come up together and agree on a rendezvous before step one — a partial cluster is a stuck job — and the group has to be torn down and rebuilt cleanly if anything fails.

How VaultLayer runs it

VaultLayer provisions the nodes as a group, establishes inter-node networking and the rendezvous, and launches your distributed script. If a node becomes unhealthy or is reclaimed mid-run, VaultLayer recovers the whole group from the last checkpoint rather than leaving you with a half-dead cluster to debug. Standard PyTorch DDP and FSDP and DeepSpeed scripts work; you run the same vl run entrypoint you use for single-GPU jobs.

Multi-node is in early access — request access to scope it with your team.

When you need it

• The model plus optimizer state is too large for a single GPU and needs FSDP sharding across nodes.
• You want higher throughput via data parallelism over more GPUs than one box holds.
• A large training set means single-node wall-clock time is the bottleneck.

If a single GPU still fits your job, fault-tolerant single-node training is simpler — multi-node is for when you've outgrown one machine.

Frequently asked questions