RigidNode™ — The Homelab Community Page

Three Connector Families. One Platform.

Each board family is a distinct PCB design. Same software stack, different compute ceiling. Designed in parallel.

Standard — v1 Shipping

RigidNode Standard

260-pin SO-DIMM · 69.6mm × 45mm

Orin Nano Super67 TOPS / 8GB
Orin NX 8GB70 TOPS / 8GB
Orin NX 16GB ← v1157 TOPS / 16GB

One carrier board design accepts all three modules. Upgrade from Nano → NX 16GB by swapping the module — no new board, no new enclosure.

From $1,999

v1 hardware in development — KI custom carrier board (v2) in PCB design

Pro — Parallel Development

Pro tier

699-pin Mezzanine · AGX Orin form factor

AGX Orin 32GB ← entry200 TOPS / 32GB
AGX Orin 64GB275 TOPS / 64GB
AGX Orin Industrial275 TOPS / 64GB

Different carrier board — same enclosure family, same software stack. 32GB makes 3–4 concurrent AI users real. 64GB is GPT-4 class locally. This is the true family-of-4 tier.

Contact for Pricing

Carrier board in parallel PCB design — AGX Orin reference design confirmed

Ultra — Parallel Development

RigidNode Ultra

Jetson Thor connector · Blackwell GPU · Aug 2025

T4000 ← entry1,200 TFLOPS / 64GB
T50002,070 TFLOPS / 128GB

Third connector family — third carrier board. Blackwell GPU, 7.5× the compute of AGX Orin. Module alone costs $2,000–$3,000. This is the defense, enterprise, and advanced robotics tier. Not a home product.

Contact for Pricing

Architecture defined — carrier board design follows Pro completion

Why three in parallel: The carrier boards share a design methodology and an enclosure family. Designing all three now costs almost nothing extra vs designing one. The alternative is redesigning the enclosure three separate times over three years. Same enclosure footprint. Different boards. Different modules. One manufacturing line.

What Actually Runs Inside

Every service as a Docker container on the Jetson host. Identical across all three tiers — only the model class changes with RAM.

AI Inference

Ollama + Open WebUI

docker: ollama · open-webui

Local LLM inference on dedicated NVIDIA AI silicon. No API calls. No data leaving the device. Model class scales with RAM — 13B on Standard, 34B on Pro, 70B+ on Ultra.

Standard: 13B max. Pro: 34B. Ultra: 70B+ runs comfortably.

Private Cloud Storage

Nextcloud + Samba

docker: nextcloud · samba

Family file sync and photo backup. Replaces iCloud and Google Drive. SMB shares to Windows and Mac. Storage tier scales per edition.

v1: 6TB WD Blue internal. Pro/Ultra: NAS expansion configurable.

Media Server

Jellyfin

docker: jellyfin

Open-source, no Plex Pass, no remote access paywall. Hardware transcoding via NVDEC on all Jetson platforms. Streams to every device on your network.

Jellyfin app availability varies by TV brand — known limitation.

Network Security

AdGuard Home

docker: adguardhome

DNS-level ad and tracker blocking for every device on the network. No client installs. Xbox, smart TV, and phone all benefit automatically.

Covers the whole house, not just the browser.

Remote Access VPN

WireGuard

docker: wireguard

WireGuard VPN server for secure remote access. Access Nextcloud, Jellyfin, and the AI from anywhere without exposing ports.

Self-hosted — no Tailscale dependency, no $160M-VC enshittification risk.

Intrusion Detection

Suricata IDS/IPS

docker: suricata

Network intrusion detection and prevention. Monitors traffic, alerts on threats, blocks known malicious patterns. Runs quietly in the background.

Requires tuning for low false positives on first run.

Photo Management

Immich

docker: immich

Self-hosted Google Photos replacement with ML-powered face recognition running locally on the Jetson NPU. Your family photos don't train anyone's model.

Fastest-growing self-hosted app for a reason.

Monitoring

Uptime Kuma + Glances

docker: uptime-kuma · glances

Service health monitoring and system metrics. Get alerted when something goes wrong. View CPU, memory, network, and disk from any browser.

Add your own Prometheus/Grafana stack if you prefer.

Add Your Own

Standard Docker Compose

docker compose up -d

Standard JetPack Linux OS. If a container runs on ARM64, it runs on RigidNode. We don't lock the app store. Bring your existing compose files.

Portainer available if you prefer a GUI. SSH access always open.

Standard Tier — v1 Hardware Spec

Real parts, real prices. Off-shelf v1. Custom KI carrier board in v2. Pro and Ultra boards in parallel design.

AI Compute	Jetson Orin NX 16GB — 157 TOPS	Dedicated AI silicon. Not a GPU bolted onto a PC.
Unified RAM	16GB LPDDR5	Ceiling of the 260-pin family — Orin NX 16GB is the top.
AI Model Class	Llama 13B / Qwen 14B (Q4)	Genuinely useful. Not GPT-4. Not an 8B toy either.
Inference Speed	~30–40 tok/sec (13B Q4)	Conversational. 2 concurrent users comfortable.
OS Storage	1TB NVMe (NV3 M.2)	OS + AI models + Docker. Slot 2 open for expansion.
NAS Storage	6TB HDD (CMR 3.5")	Internal. ~$26.67/TB sweet spot.
Router	ER605 V2 — dedicated silicon	Hardware-isolated. Jetson reboots don't drop the network.
Switch	8-port Gigabit (TL-SG108)	6 open ports for APs and wired devices.
WiFi	2× EAP610 WiFi 6 APs	Wall-mounted. Omada SDN. Whole-home coverage.
Power	12V 10A single-rail PSU (120W)	Peak load ~53W. 2× headroom.
Enclosure	Custom — KI Designed & Manufactured	Aluminum. Shared form factor across all three tiers.
v1	$1,999 estimated MSRP target	Off-shelf. Margin improves on v2 custom carrier.

v2 Standard carrier: RTL8370N switch chip + native SATA + clean 12V distribution on one board. Eliminates Waveshare + TL-SG108 + USB-SATA adapter. Designed in SolidWorks PCB, manufactured by JLCPCB. Pro and Ultra carrier boards in parallel design using the same AI toolchain.

Honest Limitations — Standard Tier

These are the Standard (260-pin) tier limits. Pro and Ultra resolve most of them at higher cost.

✓ What it does well

✓ Local AI for a family — 13B runs privately, no API calls

✓ Replace iCloud, Google Photos, Dropbox with your own hardware

✓ Network-level ad blocking — every device, no client install

✓ Jellyfin replaces Plex — no remote access paywall

✓ Hardware-isolated router — AI crashes don't take the network down

✓ Full function with zero cloud subscriptions

✓ Standard Docker — add any ARM64 container

✓ WireGuard VPN — self-hosted, no Tailscale

✓ SSH always available — you own the hardware

✗ Standard tier limits (Pro/Ultra resolve)

✗ 70B models — 16GB RAM is the 260-pin ceiling. → Pro (32–64GB) resolves

✗ 4+ concurrent AI users — 2 comfortable, 4 queues → Pro resolves at 32GB

✗ AI training/fine-tuning — inference only → Ultra (Thor) resolves

✗ Self-hosted email — don't. Nobody wins that fight. Not resolved by any tier.

✗ 10GbE — GbE throughout. Enough for family NAS. → Pro/Ultra adds 25GbE

✗ RAID redundancy — single drive on v1. Backup is your responsibility.

✗ App Store abstraction — Docker Compose, not one-click Umbrel

✗ First-gen hardware — v1.1 will follow. Early adopters accept this.

Complete Platform — All Three Board Families

Every module across every connector family. This is the full product map being designed in parallel.

Module	Connector	RAM	AI Compute	Max Model	ChatGPT / Claude	RigidNode Tier
Standard Family — 260-pin SO-DIMM — One carrier board, all modules accepted
Orin Nano Super 67 TOPS	260-pin	8GB	67 TOPS	Llama 8B	Free / Free	Entry / RigidHearth
Orin NX 8GB 70 TOPS	260-pin	8GB	70 TOPS	Llama 8B	Free / Free	Standard Base
Orin NX 16GB 157 TOPS — ceiling of this family	260-pin	16GB	157 TOPS	Llama 13B	Plus / Pro	v1 Target
Pro Family — 699-pin Mezzanine (AGX Orin) — Separate carrier board
AGX Orin 32GB 200 TOPS	699-pin	32GB	200 TOPS	Llama 34B	Pro / Pro Max	Pro Entry
AGX Orin 64GB 275 TOPS	699-pin	64GB	275 TOPS	Llama 70B	GPT-4 / Claude Full	Pro Max
Ultra Family — Jetson Thor connector · Blackwell GPU · Aug 2025 — Third carrier board
Jetson T4000 1,200 TFLOPS FP4	Thor	64GB	1,200 TFLOPS	70B+ concurrent	Beyond subscription	Ultra Entry
Jetson T5000 2,070 TFLOPS FP4 — Blackwell	Thor	128GB	2,070 TFLOPS	Multiple 70B concurrent	Beyond subscription	Ultra Max

* ChatGPT / Claude column shows approximate cloud subscription tier with equivalent capability. Local inference — your data never leaves the hardware.

Connector compatibility note: The 260-pin family accepts Orin Nano and Orin NX modules interchangeably — module swap, no new board. The 699-pin family accepts AGX Orin 32GB, 64GB, and Industrial — module swap, no new board. Thor modules are exclusive to the Thor connector family. Crossing families requires a new carrier board. This is the correct architecture — each tier is a clean product SKU with a defined upgrade cliff.

GPU

PC Discrete GPU — Local AI Comparison

Same columns. Different architecture. Requires a full desktop PC host — not a standalone device.

Module	Connector	RAM (VRAM)	AI Compute	Max Model	ChatGPT / Claude	RigidNode Tier Equivalent
RTX 40 Series — Ada Lovelace — PCIe x16
RTX 4060	PCIe x16	8GB VRAM	~136 TOPS	Llama 8B	Free / Free	Entry
RTX 4070	PCIe x16	12GB VRAM	~165 TOPS	Llama 13B	Plus / Pro	Standard
RTX 4080	PCIe x16	16GB VRAM	~780 TOPS	Llama 13B	Plus / Pro	Standard
RTX 4090	PCIe x16	24GB VRAM	~1,321 TOPS	Llama 34B	Pro / Pro Max	Pro Entry
RTX 50 Series — Blackwell — PCIe x16
RTX 5060	PCIe x16	8GB VRAM	~612 TOPS (FP4)	Llama 8B	Free / Free	Entry
RTX 5070	PCIe x16	12GB VRAM	~838 TOPS (FP4)	Llama 13B	Plus / Pro	Standard
RTX 5070 Ti	PCIe x16	16GB VRAM	~1,024 TOPS (FP4)	Llama 13B	Plus / Pro	Standard
RTX 5080	PCIe x16	16GB VRAM	~1,421 TOPS (FP4)	Llama 13B	Plus / Pro	Standard
RTX 5090	PCIe x16	32GB VRAM	~1,792 TOPS (FP4)	Llama 70B	GPT-4 / Claude Full	Pro Max

* Discrete GPU cards require a full desktop PC — CPU, motherboard, OS, and power supply not included. VRAM is the primary limit for local model size. AI TOPS not directly comparable to Jetson unified memory architecture. RTX 50-series TOPS figures use FP4 precision — not directly comparable to RTX 40 series INT8 TOPS or Jetson unified memory architecture TOPS. Host PC OS telemetry and cloud connectivity mean discrete GPU inference is not sovereign by default.

Built for Families First. Opened to Everyone.

RigidNode is the hardware foundation. Every KI capability runs on it. Subscriptions are the optional cloud and AI module layers — your hardware runs fully local without them.

I’m a solo dad. Three kids at home — Liam builds and scans, Kathryn makes digital art, Emily runs operations. Connor manages the archive remotely. Their photos, their creative work, four generations of family heritage, and the AI that makes sense of it all — it lives on hardware we own, governed by Three Laws I designed, in a building we control.

My great-grandfather Thomas came to America, got a mechanical engineering degree from the University of Detroit in 1931, built The Wood Shop, and watched industrial scale take it. That was the founding wound. I built KI so nothing we make gets taken.

Layer 0 costs nothing beyond the hardware. Everything local, everything sovereign, everything governed. Here’s what the ecosystem looks like when you’re ready to expand.

— Shaun Kavanagh, Founder

Layer 0 — Local Only

Free

Hardware purchase only. No cloud dependency.

✓ Full local AI inference — 13B models at real-time speed
✓ Local NAS, Nextcloud, Jellyfin, Immich
✓ Docker stack — bring your own containers
✓ RigidTrust Three Laws governance on every inference
✓ SSH always open, full Linux access
✓ No subscription required. Ever.

Layer 1 — Explorer

$5 /mo

Add the sovereign cloud layer.

✓ Everything in Layer 0
✓ RigidVault Cloud — 100GB Michigan sovereign storage
✓ RigidMarket ecosystem access — 140 modules
✓ Off-site encrypted backup via RigidVault tunnel
✓ Heritage assets protected — never auto-deleted on cancellation
✓ Cancel anytime. Hardware keeps running.

Start at Explorer →

Layer 2 — Creator & Above

$49 – $199 /mo

The full 15-pillar KI ecosystem.

✓ Everything in Explorer
✓ 500GB to 5TB RigidVault storage
✓ Full RigidAI module suite — photo AI, heritage docs, generative design
✓ RigidRoyalty opt-in for creators
✓ RigidPulse shop floor data integration
✓ Advanced vault namespace, SLA guarantee, white-glove migration

See full RigidVault tiers →

Heritage assets are never deleted on cancellation. That protection is permanent across all tiers. We make money on subscriptions chosen freely — not on paywalling hardware you already own. KI was built for three kids in Clinton Township, Michigan. It works for yours too.

Who's building this

Shaun Kavanagh

Founder & CEO — Kavanagh Industries LLC

30 years of mechanical systems engineering — AutoCAD at 9, first professional CAD job in 1996, GM Global Technical Operations through 2025. Five patent filings. I am not a software person who decided to do hardware. I am a mechanical systems engineer who got tired of watching good hardware get buried under subscription paywalls.

I'm Q1 now. I have the UniFi stack, the NAS, the Jetsons, the whole thing. I've been Q2 more times than I can count — garbage ISP router, paying Plex $7/month, photos on Google's servers because there was no real answer. Nobody built the Q2 device. So I'm building it.

RigidNode Home Node is for Q1. Plugs into your existing stack. Doesn't touch your router. Dedicated local AI inference, sovereign storage, runs whatever Docker containers you bring. RigidNode Complete is for Q2. One box, one cable into the wall. Everything included. The device I needed every time I moved into a new place with nothing and no good answer.

Years CAD/Engineering

Patent Filings

Boards in Parallel

Apr 2026

We're Building This in Public

The homelab community will tell us where we're wrong faster than any QA team.

Where to Find Us

We're not here to pitch you. If you're Q1 — you already have a network, you're running UniFi or OPNsense or whatever you've built, you just want a dedicated local AI node to complete your stack — that's the Home Node. Plugs into your switch. Doesn't touch your router. If you're Q2 — ISP router, nothing built yet, want to replace everything in one box — that's RigidNode Complete, coming soon. Either way: tell us what we got wrong. Tell us if the Orin NX 16GB is the wrong module for the plug-in tier. Tell us what's missing from the Docker stack. We're listening before we're selling.

kavanaghind.com Full Lineup Home Node RigidVault RigidAI Modules Email Shaun

r/homelab

Show and tell incoming — actual hardware photos, enclosure design, power architecture. The plug-in AI node that doesn't touch your UniFi setup. Hard questions welcome.

r/selfhosted

Docker Compose stack going public on GitHub. See exactly what runs. Bring your own containers. Tell us what's missing. No black box.

r/LocalLLaMA

Orin NX 16GB vs AGX Orin 32GB vs Mac Mini M4 benchmark numbers coming. Real tok/sec on real models. No cherry-picking.