// LATEST JOURNAL · Blitz Ultra RTX — Content Creation & Gaming
// On-Prem AI · Edge Compute 24 GB VRAM · 256 GB RAM
// AI Series

Datacenter-class AI
on your desk —
or off the grid.

Run large language models, fine-tune networks, and process sensitive datasets entirely on-device. No cloud bills, no data leaving the room — a fully configurable AI workstation with the VRAM and memory to do real work.

Raptor X18
// 01

Local AI, Serious Hardware

24 GB of VRAM

NVIDIA RTX 5090 mobile graphics run large quantized LLMs, diffusion models, and vision pipelines entirely on-device.

256 GB System Memory

Load full datasets, long contexts, and multiple models at once — with ECC options for runs that can’t silently fail.

Private by Default

Air-gap capable. Your data, weights, and prompts never leave the machine — compliance teams sleep at night.

// 02

AI Use Cases

01 / 06
Private AI
Local LLM Inference & RAG
02 / 06
Training
Model Fine-Tuning & Evaluation
03 / 06
Edge AI
Computer Vision at the Edge
04 / 06
Analytics
Data Science & Analytics
05 / 06
Compliance
Secure AI for Regulated Teams
06 / 06
Robotics
Robotics & Embedded Development
// The AI Workstation
Raptor X18

Intel Core Ultra 9 290HX with 24 cores, NVIDIA RTX 5090 graphics with 24 GB of GDDR7 for on-device inference, up to 256 GB of DDR5-5600 ECC memory, and four NVMe slots with RAID 0/1/5 for fast dataset pipelines — datacenter-class AI hardware in an 18.4″ chassis.

Raptor X18
EUROCOM // RX18
CPU
Intel Core
Ultra 9
290HX · 24C / 32T
GPU
NVIDIA
RTX 5090
24 GB GDDR7
Memory
Up to
256 GB
DDR5-5600 ECC
Storage
4 × NVMe
Slots
RAID 0/1/5 · Gen5
Bench: RX18 vs. Consumer Laptop
+312%
// 03

Your Data Stays Yours

The strongest AI privacy policy is physics: inference that never touches a network can’t leak.

01

Air-Gapped AI

Run inference and training with radios disabled at the firmware level — a sealed environment for classified and client-confidential data.

02

Encrypted Everything

TPM 2.0, self-encrypting NVMe, and full-disk AES-256 protect datasets, weights, and outputs at rest.

03

Linux-Native

Ships your way: Windows 11, Ubuntu, or dual-boot, with full support for CUDA-based stacks — PyTorch, TensorFlow, Ollama.

// 04

From Prototype to Fleet

One machine for a researcher or forty for a lab — configured, imaged, and supported the same way.

Sized to Your Models

Tell us your target models and batch sizes; we spec VRAM, memory, and storage to the workload instead of selling you the biggest number.

Stack-Ready Out of the Box

Configured and burn-in tested with your framework stack, so day one is spent training — not troubleshooting drivers.

Team Deployments

Volume terms and standardized, configuration-locked builds for research groups, startups, and regulated teams.

Grows With the Field

User-upgradeable memory and storage plus long-term parts availability — adapt the hardware as models get bigger.

Trusted by Professionals

Caleb Walker
Caleb Walker
Director & Co-Owner, 123 Cyber

Canadian Armed Forces veteran and co-owner of 123 Cyber, focused on compliance and capability for Canada’s Defence Industrial Base.

“Our operators need to capture what is happening the moment it happens, without worrying about a connection or where the data goes. Running 123 Capture on a sovereign, Canadian-built machine means the record stays in Canadian hands, from the first spoken word.”
Caleb Walker · Director & Co-Owner, 123 Cyber
Ravi Ramsaran
Ravi Ramsaran
Chief Product Officer, Nextria

Product lead for CastleGuard AI, Nextria’s sovereign, air-gapped mission-AI platform, designed and built in Canada.

“Our AI-in-a-box system equips Canadian defense organizations with mission-ready applications, critical intelligence and relevant insights tailored to their unique needs. CastleGuard AI is a truly Canadian invention, designed and developed in Canada, and delivered on a Canadian-built supercomputer with unique IP that bolsters our sovereign AI capabilities.”
Ravi Ramsaran · Chief Product Officer, Nextria
// Deploy Locally

Ready to Bring AI In-House?

Talk to our AI specialists about sizing a local workstation to your models, your data policies, and your budget.