What is compared
Conventional: most operational tasks are sent to a large language model. NeuralOps: detached systems and rules handle scans, backups and validation; a model is used only when language or judgement is required.
Demo · website-ops carbon compare
Estimate monthly CO₂e of conventional LLM-first website management versus NeuralOps detached-first routing. Same formula as the AINNA Carbon Emulator.
ESG · compute efficiency
Carbon print: NeuralOps vs conventional website operations
This section estimates the compute energy and CO₂e of managing a hospital website and journal — audits, drafts, SEO, link checks, logs — not the carbon of every public page view. Figures use the same layer model as the AINNA Carbon Emulator.
Grounded factors
Grid factor default 0.74 kg CO₂e/kWh, PUE 1.4, and kWh per 1,000 requests by layer — all defaults from the AINNA Carbon Emulator. Token reduction of up to 87% is an internal benchmark on a tested language workload, not a hospital-site measurement.
What this is not
Not a certified carbon audit. Not a claim of KPMC’s actual emissions. Not a guarantee of 87% reduction on every task. Adjust the sliders; the model recalculates live.
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— kWh
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— kWh
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— kg CO₂e / month
— kg / year
≤87%
Internal benchmark on tested token workload — applied only as context, not multiplied into the kg figure.
| Layer | What it represents for website ops | kWh / 1,000 tasks | Conventional share | NeuralOps share |
|---|---|---|---|---|
| AI Berat GPU | Full LLM for every rewrite, scan ringkasan or log read | 0.15 | 70% | 5% |
| AI Ringan / CPU | Short classification or title suggestion | 0.05 | 20% | 15% |
| Berasaskan Peraturan | Validation, metadata, schema, spelling lists | 0.01 | 8% | 20% |
| Detached system | Link crawl, sitemap, backup check, uptime probe | 0.005 | 2% | 60% |
Estimate / simulation only. Formula: tasks × layer share × (kWh per 1,000 tasks) × PUE × grid factor. Sumber: AINNA Carbon Emulator defaults. Perubahan any input to see sensitivity. Do not treat the result as audited hospital ESG data.
