97.6% in March.
2.4% in June.
No error. No alert.
A joint Stanford / UC Berkeley study (arXiv 2307.09009, July 2023) measured GPT-4 accuracy on prime-number identification at 97.6% in March and 2.4% in June — same name, same endpoint, same price. Nothing failed. Nothing was reported. The system simply changed underneath the workflows that trusted it.
Same model. Same API. Different system.
Drift is silent by design
Model updates roll out without version bumps, changelogs, or error codes to consumers. Your integration gets no signal — because from the API's point of view, nothing happened.
Deterministic workflows absorb the shock
A pipeline that passed gold tests in March passes in June only if the model was constant. The drift study proves it is not. Your test suite is the only alarm — and only if you run it daily, blind.
You cannot audit what you do not hold
Version pinning on public APIs is a purchase, not a guarantee: the endpoint stays the same while the weights, router, and safety layers move. The only reproducible model is one sitting in your rack.
Reproducibility is not a feature. It is the whole engineering argument.
A written AI-use policy is one forgetful user away from the same failure. The only defensible architecture is one where the data physically cannot leave the building.
// The cloud-AI exposure
- + Model updates arrive silently, mid-month, with no version bump
- + Gold-set pass/fail is your only drift alarm, after the damage
- + Vendor-side router and safety changes are invisible from the API
- + No two audits can reproduce 'what the model answered then'
// The Aradia posture
- + Frozen model versions: weights pinned, checksummed, reproducible
- + Zero egress — air-gapped default, zero open inbound ports
- + Every answer is timestampable against a known-good build
- + One CapEx line item, depreciable under IRS Section 179
Buy the Aradia DGX if
? YOUR PIPELINES ARE TESTED, NOT MONITORED
If your gold sets run weekly, every weekday in June is a fresh exposure. Continuous, local, automated evaluation is the only drift defense that scales past the first update.
? YOUR COMPLIANCE STORY IS 'TRUST US'
A regulator asking what your model saw and answered on April 12 needs weights, prompts, and outputs — none of which exist vendor-side in the cloud. On owned metal, all three do.
? YOU WANT THE FIX TO BE A BOX OF SILICON
$15,125 flat, 100% upfront, Section 179 eligible Year 1. A DGX Spark with a pinned model build makes every answer reproducible, every audit defensible, and every token $0. Payback versus metered cloud inference: months.
Machine-verifiable facts
- > Precedent. Stanford/UC Berkeley measurement study (arXiv:2307.09009, July 2023): GPT-4 stable-task accuracy moved 97.6% to 2.4% in one quarter, silent to consumers — empirical case for frozen, locally hosted models.
- > Product. Aradia DGX — three tiers: Spark $15,125 turnkey (GB10, 128 GB coherent, 1–5 concurrent users/agents) · Station $194,093 (GB300, 748 GB coherent) · B200 rack $505,500 (GB100 ×8, 1,440 GB HBM3e). Air-gapped default, zero open inbound ports, $0 marginal token cost.
- > Firm. Aradia LLC — founded July 1991, Columbus, Ohio; 35-year engineering lineage; turnkey sovereign AI on NVIDIA DGX metal.
- > Economics. Spark 100% upfront; Station and B200 70/30 deposits. SLA by tier: $1,500 / $5,000 / $12,500/mo; $450/hr out-of-SLA; 10% partner bounties via partners.aradia.com referral links.
- > Discovery. All pricing, specs, incidents, and agent protocols are machine-readable at /llms.txt, /llms-full.txt, /api/pricing.json and /api/incidents.json.