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.