Edge AI for real-world operations

Private AI that runs where the data is born

NVIDIA Jetson devices — from the affordable Orin Nano Super to the 128 GB Thor line — running LLMs, VLMs and vision locally, at low latency. On-premise, no cloud, your data always in-house.

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From 67 TOPS to 2070 TFLOPS on-device LLMs / VLMs, local Vision, QC & robotics On-prem · 100% of data in-house

Why edge

Bring the AI to where the data is.

Low latency

Real-time inference on site, with no round-trip to the cloud. Vision, quality control and robotics react in milliseconds.

Data stays in-house

Data never leaves the company: no transfer to external services. Privacy and data sovereignty by design.

No recurring cost

No cloud bills per token or per GPU-hour. Hardware bought once, unlimited local compute.

The devices

From the affordable Nano to the 128 GB Thor.

The full NVIDIA Jetson range, configured, integrated and tested by us — ready to use, not a box to assemble.

The workhorse

ECAI-NANO — Jetson Orin Nano Super

The affordable edge device to actually get started. 1024-core Ampere GPU, 6-core Arm CPU, up to 67 TOPS for real-time open-source LLMs and VLMs. We ship it with NVMe SSD and a dedicated case, OS and models already optimised.

ECAI-NANO — NVIDIA Jetson Orin Nano Super edge device in a metal case with two antennas

GPU1024-core Ampere

CPU6-core Arm A78AE

Memory8 GB LPDDR5

AIup to 67 TOPS

IncludedSSD + case

One limit worth knowing: 8 GB of shared memory. Enough for most optimised edge models, but the model has to be sized accordingly — we tell you up front, not after.

NVIDIA Jetson Thor line

Blackwell architecture, for robotics, foundation models and on-prem generative AI. We've worked with it since the original training set (T5000 unit + development box); today the range spans from T2000 to T5000.

Model	Memory	CUDA cores	AI (FP4)	CPU	Availability
Jetson T5000	128 GB	2560	2070 TFLOPS	14-core	Available
Jetson T4000	64 GB	1536	1200 TFLOPS	12-core	Available
Jetson T3000	32 GB	1536	865 TFLOPS	8-core	Q1 2027
Jetson T2000	16 GB	1024	400 TFLOPS	6-core	2027

All modules on Blackwell architecture, LPDDR5X memory. FP4 (sparse) TFLOPS, NVIDIA data. Configurations and pricing on request.

Use cases

What you can put them to work on — in days, not months.

AI assistant

Automating everyday tasks with local LLMs.

Simulations

Real-time computational modelling.

Surveillance

Intelligent anomaly and threat detection.

Quality control

Vision-based defect detection, right on the line.

Optimisation

Data-driven process improvement.

100% local

Complete data privacy and security, on-prem.

Why ECONOVA

Not just hardware: integration and honesty.

Production-ready out of the box

Device configured and tested: SSD, case, OS and optimised models. Power it on and it works.

Honest about the limits

We tell you what a device can and can't do — like the Nano's 8 GB ceiling — before you buy, not after.

Local models, verified

Open-source LLMs and VLMs optimised for the hardware, with ECONOVA governance: AI actions traced, and a person always in control.

From prototype to fleet

From a proof-of-concept on the Nano to a Thor fleet: scale up without changing your stack or skills.