infra · NVIDIA
AI Infrastructure & MLOps with NVIDIA Jetson
AI Infrastructure & MLOps built on NVIDIA Jetson, chosen where it genuinely fits, and swapped where it does not.
- Category
- infra
- Vendor
- NVIDIA
- Alternatives we also use
- 7
Why NVIDIA Jetson for this
Cost per inference is the operating metric. We instrument it from day one so capacity decisions are made on evidence.
NVIDIA Jetson is strongest at real-time inference on site with no network dependency. For ai infrastructure & mlops that matters because the failure modes of this kind of system tend to cluster exactly there.
The honest trade-off: model size is constrained by the module you choose. We say that up front because a stack chosen for fashion rather than fit becomes someone's migration project two years later. Every engagement opens with a measurement: the cycle time, the cost per transaction, or the error rate we are being asked to move.
You own the code, the models where they are open-weight, and the documentation to run it without us.
The honest assessment
- What it is
- Edge AI hardware for running vision models where the cameras actually are.
- Strongest at
- real-time inference on site with no network dependency
- Trade-off
- model size is constrained by the module you choose
- Category
- infra
We are not a reseller for NVIDIA and hold no commission on this choice. Where a different option fits your workload better, the recommendation will say so. That is the entire value of asking us.
What is included
- Workload sizing based on measured throughput, not guesses
- Model registry and versioned deployments
- Autoscaling and cost-per-inference monitoring
- Canary and rollback deployment paths
- On-premise or air-gapped options where required
- Runbooks and on-call documentation
Questions
Cloud or on-premise?
We model both against your real volume. On-premise typically wins at sustained high throughput or where data residency is non-negotiable; cloud wins on variable and early-stage workloads.
Can you deploy air-gapped?
Yes, with open-weight models and a fully offline inference stack, the usual pattern for defence, and for some healthcare and government work.
Do you support our existing Kubernetes setup?
Yes, and we would rather extend it than introduce a parallel platform your team has to learn.
Alternatives for ai infrastructure & mlops
Same capability, different stack. Each page states its own trade-off.
What else we build on NVIDIA Jetson
Building with NVIDIA Jetson?
Bring us the workload and we will tell you whether this is the right stack for it.
Or email bd@dtrasglobal.com · call +91 74118 77878