PyTorch models inferring on-device — server cost eliminated.
We exported PyTorch models to Core ML·ONNX for on-device inference, removing server round-trips and their cost.
View case →● DATA & ML ENGINEERING
From “it ran” to “it serves reliably in production.”
We own the entire ML lifecycle — from ingestion through deployment and operations.
100s
GPU orchestration
on-device
deployment
24/7
monitoring
● THE PROBLEM
If this sounds familiar.
If even one of these is familiar, this service closes that gap.
The notebook trap
The model works, but serving, scaling, and cost don't.
GPU waste
Expensive cards sit idle without scheduling.
Edge constraints
You need to move on-device, but conversion·optimization is daunting.
● SCOPE
What, how far, what we leave behind.
We lift models into production services, owning the ML lifecycle from ingestion to operations.
How far
- Data pipelines
- Model serving·deployment
- GPU orchestration·scheduling
- On-device·edge ML
- Monitoring·optimization
Deliverables
- Data·ML pipelines
- Deployed endpoints or on-device builds
- GPU operations framework
- Performance·cost report
● HOW WE WORK
How we work.
01
PipelineData ingestion·cleaning·features
02
ServingEndpoints or on-device builds
03
SchedulingGPU allocation·autoscaling
04
OperateMonitoring·drift·cost optimization
● EVIDENCE — NOT COPY
A record of what we shipped.
On-device ML · edge inference
on-device
inference
Core ML/ONNX
conversion
0$0
edge server cost
● STACK & DOMAIN
Stack & domain.
PyTorchONNXCore MLTritonKueueSageMakerRay
A team that has handled large-scale GPU operations and edge constraints at the same time.
● FAQ
Frequently asked.
We have the model — can you just deploy it?+
Yes. Serving·scheduling is our most common standalone request.
Does on-device really cut cost?+
The more inference traffic, the more server cost disappears — the bigger the win.
Our GPU cost is out of control.+
Scheduling, autoscaling, and visibility raise utilization and throughput per card.
Take your model to production.
A free 30-minute consult to set the direction first.