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.
Setting it up took a weekend. Getting it back should take a minute. Aquanode saves your whole setup, packages and CUDA included, then puts it back on any GPU at any provider. Leave it idle and the 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 there is capacity
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 deploy --snapshot 4242 --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.
