Offline Deployment
Run LLMs entirely disconnected from the internet. Air-gapped environments fully supported, from install to inference.
EDGE AI · INFRASTRUCTURE
Own your AI.
Own your AI.
On-premise. |
Run powerful large language models entirely offline, on hardware you control. Your data never leaves your premises. We fine-tune, customize, and deploy — or engineer your whole AI product from scratch.
AXN-EDGE / NODE-01
System Online
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Product One — Platform
Your AI, on your terms.
End-to-end edge deployment infrastructure for organizations that refuse to compromise on data sovereignty. No cloud round-trips. No third-party servers. No data leaving the building.
Data Protection
Your proprietary data never touches external servers. Full audit trails and compliance-ready logging built in.
Model Fine-Tuning
We adapt open-weight and proprietary models to your domain, vocabulary, and constraints — LoRA, QLoRA, full fine-tunes.
Full Customization
From prompt engineering to architecture tweaks — the model behaves exactly as your workflows require.
Custom Server Box
Tailored hardware configurations — from compact edge units to high-performance multi-GPU racks.
Local Machine Deploy
Deploy onto existing infrastructure. Windows, Linux, ARM, x86 — we handle the complexity end to end.
How it works
From use case to production.
A disciplined four-phase engagement. Scoped, validated, and supported.
01
Phase 01Discover
We analyze your use case, data landscape, security posture, and hardware constraints.
02
Phase 02Customize
Fine-tune, quantize, and optimize models to run fast on your specific edge hardware.
03
Phase 03Deploy
Install, configure, and validate on your local machines or a purpose-built box.
04
Phase 04Support
Ongoing monitoring, signed offline updates, and iteration as your needs evolve.
Product Two — Build
Start a Project →
We build your vision.
Beyond deployment, AxonRiedge is a full-service product engineering partner. From concept to production, we architect and ship software with AI at its core.
We design products where the model is the product — not a bolted-on feature. Architecture, evals, and UX built around inference from day one.
Frontend, backend, data, and infra under one roof. We own the whole stack so handoffs never become bottlenecks.
Hybrid systems that train in the cloud and serve at the edge — keeping sensitive inference local while scaling where it's safe.
Reproducible training, evaluation, and rollout pipelines with versioned models and signed, air-gap-friendly artifacts.
Interfaces that make probabilistic systems feel trustworthy — streaming, citations, guardrails, and graceful failure states.
Deployment Targets
Hardware that fits the job.
From a single embedded unit to organization-wide racks — or a build engineered entirely to your spec.
| Device Type | Use Case | Latency | Models Supported |
|---|---|---|---|
| Edge Mini · ARM | Single user, embedded | <50ms | Up to 7B params |
| Edge Pro · x86 | Team, real-time | <20ms | 13B – 70B params |
| Enterprise Rack | Organization-wide | <10ms | 70B+, multi-model |
| Custom Build | Your specs, your constraints | Tuned | Fully custom |
0+Edge Deployments
0%Uptime Achieved
0Data Breaches
★★★★★
"Our compliance team signed off in a single meeting. Nothing leaves the building, and the model is faster than the cloud API it replaced."
RM
R. MehtaVP Engineering · FinServ Co.
★★★★★
"AxonRiedge fine-tuned a model on our clinical vocabulary and deployed it air-gapped in under a month. Exactly what we needed."
DC
Dr. ChenCMIO · Regional Health
★★★★★
"They didn't just deploy a model — they built the whole product around it. True engineering partner."
JK
J. KowalskiFounder · Defense Startup
Field Logs
All Articles →
Notes from the edge.
cover image · 16:9
Running a 70B model on a box that fits under a desk
How quantization, speculative decoding, and the right GPU turn a rack-scale model into a single quiet edge unit.
Read log →cover image · 16:9
Shipping model updates into an air-gapped network
A practical playbook for signed artifacts, verifiable transfers, and zero outbound packets — start to finish.
Read log →cover image · 16:9
Teaching a base model your company's vocabulary
What a domain LoRA actually changes, how much data you really need, and how we evaluate before it ever ships.
Read log →
FAQ
Questions, answered.
Open-weight transformer families — Llama, Mistral, Qwen, Gemma, Phi and more — as well as proprietary checkpoints you own. We handle quantization, LoRA/QLoRA and full fine-tuning, and inference across GGUF, vLLM, and TensorRT-LLM runtimes.
Yes. We deploy to existing x86, ARM, and GPU infrastructure across Windows and Linux — or supply purpose-built edge boxes when you need dedicated hardware.
Updates ship as signed, verifiable artifacts installed through an air-gapped transfer procedure. No internet connection is required at any point in the lifecycle.
A scoped edge deployment typically runs 3–6 weeks from discovery to validated production, depending on hardware availability and fine-tuning depth.
Yes — monitoring, scheduled model-refresh cycles, and continued iteration are available as ongoing engagements after your initial deployment.
Absolutely. Beyond deployment we are a full-service product engineering partner, building AI-native products end to end — from architecture and model to interface and ops.
Contact
Ready to own your AI?
Get in touch to discuss edge deployment, custom hardware, or your next AI-powered product. We reply within one business day.
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