D-05active

AI Infrastructure & Local-LLM Setup

Run the AI where the data lives - no cloud, no leak.

local / on-prem LLM infrastructurewalled-garden (cloudless) deploymentbuild-spec + cost engineeringrunning your own AI rig

The offer

On-Prem AI Standup

scoped

Local-LLM deployment inside your walled garden. Nothing leaves.

Start with the $4,500 Teardown →

Proof

What shipped

A secure, always-on AI work environment - in daily production

Stand up the whole AI stack - compute, local models, security, backup, remote access - and actually run on it.

Situation: Using AI seriously means infrastructure: a machine that runs models locally, stays secure, is backed up, remotely accessible, and always on. Most people never get past a browser tab.
Built: A dual-machine ecosystem built from bare metal: an always-on backend tower (PowerSpec G914 - Ryzen 9 9950X3D, RTX 5090 / 32GB VRAM, 64GB DDR5-6000, 2TB + 4TB NVMe Gen4) plus a working laptop - with a local-LLM sandbox (CUDA / Docker / WSL2), a full security baseline (clean OS, disk encryption, credential vault), layered backup/DR (offsite + image + versioned), remote access (Parsec / SSH / mesh VPN), a complete dev toolchain, UPS-backed auto-recovery, and a purpose-built recording studio.
Outcome: In daily production use for ~10 weeks - client image processing, compute jobs, and file indexing all running on the local stack, no public cloud in the loop. The same pattern deploys AI tooling inside a regulated client's walled garden.

PowerSpec G914: Ryzen 9 9950X3D / RTX 5090 32GB / 64GB DDR5-6000 / 2TB+4TB NVMelocal-LLM sandbox (CUDA/Docker/WSL2)full security + backup/DR + remote accessUPS auto-recovery + recording studioin production ~10 weeks

What do I actually need to run this AI stuff?

Cut through 1,000s of YouTube videos to the actual build - hardware, software, and real cost - that gets you running.

Situation: Everyone wants to 'use AI,' but the setup is a fog of conflicting YouTube advice, mystery hardware specs, and hidden monthly costs.
Built: The know-how from actually standing up a working local-AI stack - the rig spec, the software layer, the real one-time and monthly costs, and what you can skip.
Outcome: Someone goes from 'no idea what I need' to a working setup on a known budget.

built and runs own local-AI stackknows the real cost curve

Built once, handed off clean - the whole rig is reproducible

Document infrastructure so well a second operator rebuilds it from the docs alone.

Situation: A one-off box dies with its builder. A system travels.
Built: The entire environment captured in a versioned build log plus a BOM substitutability matrix - hardware, OS baseline, security, sandbox, toolchain, the works.
Outcome: A second operator rebuilt the whole environment on his own hardware (a ~$7.1K one-tier-up BOM) from the documentation set alone - no live hand-holding. The docs traveled; the operator didn't have to.

reproducible from docs aloneBOM substitutability matrixsecond-operator clone (~$7.1K)zero hand-holding

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