Research · Distributed GPU Training
Distributed GPU TrainingAI Infrastructure

Train big.
On your own iron.

OMNI-Train is our research platform for distributed AI training: very large models trained across GPU clusters with PyTorch DDP and FSDP — the same techniques behind today’s frontier models, engineered to run on infrastructure you own.

OMNI-Trainour training platform
DDP·FSDPPyTorch-native distribution
Multi-nodeone job across the cluster
On-premsovereign by design
The research

Compute is the new bottleneck — and the new dependency.

Everyone wants to train and fine-tune models; few can. Renting frontier compute is expensive and sends your data to someone else’s cloud, while owning GPUs means fighting schedulers, node failures and idle cards that burn money silently. The models get easier every year. The infrastructure doesn’t.

OMNI-Train makes multi-node training boring — in the best way. Jobs distributed with DDP and FSDP, checkpoints that survive dying nodes, utilization you can defend to a CFO, and everything runnable on premises for organisations whose data must not leave the building. It’s the platform we use to train our own models — including Argus AI’s fire-detection networks.

The models get the glory. The infrastructure decides who gets to train them.

Research areas

Four problems. One platform.

Orchestration

Many nodes, one job

Multi-node schedulingPyTorch DDPFSDP shardingQueues & priorities

Efficiency

Every GPU earning its power bill

Utilization trackingProfilingMixed precisionCost per run

Resilience

Training that survives failures

Fault-tolerant checkpointingAutomatic recoveryElastic scalingRun monitoring

Sovereignty

Your models, your iron

On-prem clustersAir-gapped optionsData controlHybrid burst

Own your training runs.

From first fine-tune to a cluster that pays for itself — on your terms, on your hardware.

Talk to our researchers