Deploy AI models on H100, A100, H200, and AMD MI300X GPUs with up to 40% cost savings. Lightning-fast machine learning inference on enterprise GPU infrastructure.
Your Setup is that environment: files, models, packages, config. Captured once and restored onto any GPU, in any datacenter. Pin a version like a git commit. Push code straight from your terminal. Leave it idle and the compute bill stops.
Your setup runs on any of them, and moves between them.
Migration & backup
Move a workload to another provider. Or just get yesterday back.
One snapshot does both. Capture the directory that holds your environment, then restore it onto a different card, a different provider, or the same box tomorrow morning.
01 / MIGRATE
Land wherever it is cheaper
Snapshot the environment, launch on the provider you want, restore into it. There is no separate migration product — the snapshot that backs you up is the one that moves you.
02 / BACK UP
On a schedule, or when you say
Run snapshots automatically or take one by hand. Only the first carries the whole directory and later ones carry what changed, so a frequent schedule stays cheap.
03 / RESTORE
Never tied to the original box
Bring a snapshot back on a different GPU model, region or provider. Stop a box yourself and it is captured on the way out, so it comes back anywhere.
One setupThree datacenters
us-east
H100 80GB
hyperstack
eu-west
A100 80GB
massed compute
us-west
L40S 48GB
vast.ai
training-envv4
restored · models, dataset and packages, ready to train
restored · same environment, different provider
restored · same environment, a cheaper card
If a provider reclaims a running box, you recover to your last snapshot — not to the instant it died. A schedule keeps that window short.
What a setup does
Three things a rented machine can't do.
A box is disposable and yours isn't. Everything below is the same artifact seen from three sides.
Migrate, pause, or resume across any GPU datacenter.
Pause a Setup in one datacenter and resume it in another, on a different card from a different provider. Your files, models and packages come back the way you left them.
aq pause training-env → aq up training-env --gpu L40S
PROVIDER A
H100 · paused
PROVIDER B
L40S · running
✓ /root/models · 41.2 GB restored
✓ /root/dataset · 8.7 GB restored
↻ environment · ready to run
See how it works
See a setup move onto another datacenter.
The same console you get on day one, running the three actions a rented box cannot do for you.
Portable
Release
Sync
console.aquanode.io/setups/training-envConnected
training-envsetup
RUNNING
GPUH100 (80GB)
vCPU28 cores
Memory180 GB
Storage6 TB NVMe
GPU util88%
VRAM72%
massed compute · eu-central-1
vast.ai
hotaisle
vultr
runpod
41.2 GB portable set in flight
FROM
H100 80GB
hyperstack · canada-1
released
TO
L40S 48GB
massed compute · eu-central-1
running
5.0sCome back on a different provider with the same environment.
Auto-pause
The box you forgot about pauses itself.
Under 5% GPU for 30 minutes and you get a warning. At 60 the Setup saves itself and the machine is released. Your compute bill stops there — and everything comes back when you resume.
Storage for the saved Setup keeps billing — only the compute stops. Turn auto-pause on per Setup.
GPU 84% · ACTIVEGPU 3% · IDLE 30 MIN · WARNINGGPU 3% · IDLE 61 MIN · PAUSED
compute → $2.40/hrcompute → $0.00/hr
Bring your own hardware
Already have the machines? Use them.
If you hold a multi-year lease, the last thing you need is somewhere else to rent. Setups work on hardware we never provisioned.
01 / IMPORT
Turn a box you own into a Setup
aq import surveys the machine, captures it, and registers it as a Setup — versioned, forkable, and launchable on any provider we support.
aq import --dry-run
02 / RUN IN PLACE
Setups on your own machines
Point Aquanode at a box you already pay for. Capture, restore, versions, run and logs — against your own storage bucket, with no account required. Or connect it to the console and get sharing, metrics and endpoints on hardware we never rented.
aq host add lab-01 --ssh root@10.0.4.7
Release a machine and it keeps running. We revoke our credentials and drop the record — we never touch your box.
GPU observability
Every reading straight off the card, while the job is still running.
Utilization, VRAM, temperature and power on a running deployment, refreshed every few seconds — and a reading the card cannot give you says so, instead of showing a zero.
console.aquanode.io/vms/llm-finetune-01 · Metrics
Active
This GPU
Utilization84%
Memory used64.2 / 80 GB
Temperature73 °C
Power draw433 / 700 W
SM clock1,845 MHz
Memory clock2,619 MHz
The host it sits on
CPU utilization32%
System memory104 / 180 GB
Utilization · sessionlive window · ~10 min
This card reports every field — utilization, memory, clocks, temperature and power.
