NeoCloud / GPU cloud · First release
One failed GPU stopped 152 GPUs. Which job, which customer, what per hour, where is the last checkpoint?
You rent GPUs and must keep expensive jobs running. SynapseDC joins the building, the fabric, the GPU and the job into one picture, so a CDU flow drop or a power cap reads as GPU time lost, and a stalled job opens a Critical incident within seconds of the Xid.
Your business in SynapseDC
Your words, your units, your limits.
What is soldReserved GPU clusters and on-demand GPUs, billed per GPU-hour
MonitoredNode BMC, per-GPU DCGM telemetry, fabric rails, scheduler job metadata
NeverWorkload data, model weights, code or job contents
Premium tierReserved customers, SLA 99.9 / 99.5
Capacity unitGPUs
The GPUs are yours, so SynapseDC monitors GPUs and jobs. It never reads a job's data, model or code.
Pains we answer
The stories a demo is built around.
Xid 79 on one GPU stalls a 152-GPU training run: lost per hour, work to redo since the checkpoint, the customer and the recovery plan.
| Pain | How SynapseDC answers it | Where |
|---|---|---|
| GPU teams can't see the building | CDU flow, power caps and rack heat joined to the job they slow down | GPU Fleet |
| One bad GPU stalls a whole job | Blast radius, GPU time lost and work to redo since the checkpoint | GPU Fleet |
| Idle allocations nobody notices | Idle allocated GPUs and their cost per hour | GPU Fleet |
| Power is the biggest cost | Power per sold GPU-hour against the price, take-or-pay floors, demand ratchets | Capacity & power economics |
| Where to place the next big job | Pods ranked by free healthy GPUs, headroom, cooling and power already paid for | GPU Fleet |
| Crypto-mining or rogue devices on the fabric | Detections on GPU nodes, OOB and BMCs | Security Ops |
Modules
The screens you will use most.
See your own estate in it.
Bring the fault your team keeps dealing with. We walk it end to end on a demo estate shaped like yours, then agree what a one-site pilot should prove.