Your science shouldn't
restart from zero.
Drug discovery runs for days. The state lives on the GPU.
Each one keeps its state in GPU memory, not a database. When the GPU goes away, the work goes with it.
Structure prediction
The first step in nearly every modern discovery pipeline, with state held on the GPU across long multi-GPU runs.
- Coordinated checkpoints across all GPUs (NCCL/RDMA)
- A failed node resumes the run, never restarts it
- Drain for maintenance without losing GPU-weeks
Six ways a shared cluster loses the work.
Maintenance windows
A reservation holds every job near a quarterly patch. The cluster idles on both sides; jobs that slip through die.
Hardware failures, compounding
H100s fail roughly 8-9% a year, and bigger runs fail faster. One failure loses the whole run.
Over-allocation defense
Scientists oversize jobs to dodge an OOM crash. The dashboard looks busy; the cluster isn't.
Fragmentation & queue waits
Odd shapes don't fit the standard 8-GPU node. GPUs strand and researchers wait hours.
Spot & cloud-burst risk
Spot is up to 90% cheaper but evicts in two minutes. Long runs can't risk it.
Preemption for priority
A high-priority job arrives and the scheduler kills a long run. The work is lost, not paused.
More science from the same GPUs, nothing for researchers to change.
Six benefits, each one earned by a specific piece of the architecture, not a slogan.
No work lost to a failure or a patch
Twice the science from the same cluster
Nothing changes for your researchers
Training is protected from failures
Access cheaper, burstable capacity
Fits your regulatory posture
Your run survives the GPU underneath it.
Cedana runs at the OS and kernel level and works transparently with Slurm or Kubernetes, keeping every run moving across the GPUs underneath it.
Running
A run lives in GPU memory for days.
Save
Cedana snapshots full GPU state, below the app.
Migrate
A fault or drain triggers a live migration.
Resume
It resumes at the same step. Nothing lost.
vMotion for GPU workloads. Researchers never touch it; their runs just stop dying.
Runs everywhere your GPUs already live.
Questions we hear first.
Command your
compute.
Your compute, liquid — checkpoint, migrate, and resume live GPU jobs across the fleet.