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AINNA NeuralOps

Carbon Footprint
Emulator & Calculator

Estimate how smart routing, rule-based processing, detached systems, and GPU-only-when-needed architecture can reduce digital carbon footprint. A controlled study estimated up to 87% token reduction for its tested workload (internal benchmark). Review the study.

Efficiency Flywheel: Segmentation β†’ Smart Routing β†’ Distillation β†’ Detached Systems β†’ Private Infrastructure. Current production for suitable workloads; larger-scale ambitions are Phase 2 / funding-dependent.

ESG Framework Token Study Download ESG Report
1 Segmentation→ 2 Smart Routing→ 3 Distillation→ 4 Detached Systems→ 5 Private Infra
Calculator

Carbon Footprint Calculator

Configure your workload parameters and compare AI-Heavy vs NeuralOps Optimized processing.

Quick Presets: Auto Β· Ainna NeuralOps
Results

Dashboard

Showing preset: Ainna NeuralOps Β· auto every 3s

Energy Usage Comparison

Carbon Footprint Comparison

NeuralOps Routing Distribution

Baseline Routing Distribution

User & Workload

Baseline

AI-Heavy
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Total: 100%

NeuralOps

Optimized
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Total: 100%
Emulator

Scenario Builder

Create, save, and compare multiple workload scenarios side by side.

New Scenario

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Total: 100%

Saved Scenarios

Scenario Monthly Requests Energy (kWh) Carbon (kg COβ‚‚e) Cost Carbon Saved vs Baseline Reduction %
No scenarios saved yet. Create one on the left.
ESG

The ESG logic behind this model

This calculator translates AINNA's ESG framework into a practical operating view: where work runs, what consumes energy, and which controls can reduce avoidable compute. The outputs are modelled projections, not measured emissions or a certified ESG rating.

01 / Efficiency Flywheel

The model starts with a simple principle: use the least intensive layer that can complete the task. Segmentation and routing happen first; detached workflows remove repeat work; controlled infrastructure makes the remaining compute easier to observe. Distillation at scale and larger clusters remain Phase 2 / funding-dependent.

1 Segmentation→ 2 Smart Routing→ 3 Distillation→ 4 Detached Systems→ 5 Private Infra

02 / The operating levers

Three variables shape the projection below. Change the routing mix, PUE, or grid factor in the calculator to see how the model responds. These cards describe the mechanism, not measured AINNA emission outcomes.

Compute intensity

Model Size

Larger models generally require more compute and memory per request. Routing simple work to rules, parsers, or smaller specialists can avoid unnecessary model load.

Workload repetition

Inference Frequency

Repeated requests multiply energy use. Caching and detached systems can complete predictable work without sending the same job through a model again.

Infrastructure overhead

Infrastructure PUE

Data-centre overhead matters. The PUE field scales the energy estimate so infrastructure efficiency remains visible alongside the routing mix.

03 / ESG design commitments

E / Environmental

Environmental

  • Reduce avoidable compute
  • Use GPU capacity selectively
  • Make routing assumptions visible
S / Social

Social

  • Make efficient AI more accessible to SMEs
  • Support practical, understandable automation
G / Governance

Governance

  • Keep assumptions and limits explicit
  • Enable review of where work ran

These are design commitments, not proof of certified ESG performance.

04 / Reporting context

The framework is intended to support conversations about energy efficiency, disclosure, and responsible technology design. It does not replace legal, accounting, lifecycle, or assurance advice.

Malaysia

Malaysia

  • Energy Efficiency and Conservation Act 2024
  • National Energy Transition Roadmap (NETR)
  • Bursa Malaysia Sustainability Reporting
International

International

  • IFRS S1 & S2 Sustainability Standards
  • TCFD Β· GRI Β· SDGs
  • Paris Agreement climate objectives
ESG Framework β†’ Token Efficiency Study β†’ Efficiency Flywheel β†’ ESG Methodology Report (PDF) β†’
Learn

How NeuralOps Reduces Carbon Footprint

NeuralOps is AINNA's orchestration layer. It sends each task to the smallest sufficient system β€” rules, parsers, detached workflows, a distilled specialist, or a GPU model only when reasoning is required. That is the ESG argument modelled in this calculator.

01

Segmentation

Split inbound work into bounded tasks so a whole GPU job is not launched for a small step. Less wasted inference before routing even begins.

02

Smart Routing

NeuralOps routes to the cheapest layer that can do the job: rules first, then parser, GPU last.

03

Distillation

Move repeated capability from a large model into a smaller specialist that costs less energy to run when it is the right layer.

04

Detached Systems

Repeatable workflows run as deterministic services without an LLM in the loop for the same job twice. How Detached Systems work β†’

05

Private Infrastructure

Controlled, observed compute stays on infrastructure you can account for. Larger clusters are Phase 2 / funding-dependent.

06

Parsers & Rules

Structured input is extracted and validated by rule-based services. AI is a fallback.

07

AI Guardrails

Stop wasteful retries, oversized prompts and loops that burn tokens without changing the outcome.

08

ESG Monitoring

Watch where work ran β€” rules, detached system, or model β€” so energy use can be reviewed, not guessed.

⚠

Transparency Disclaimer

This calculator provides an estimated projection, not a certified carbon audit. Final carbon footprint should be verified using actual infrastructure logs, cloud usage reports, model runtime data, regional grid emission factors, and data centre energy metrics. The 87% token reduction figure is an internal benchmark on tested patterns, not a certified emission factor. Values may vary significantly based on hardware, optimisation level, and deployment configuration. Larger-scale NeuralOps phases are funding-dependent.

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