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AINNA NeuralOps

Jejak Karbon
Emulator & Kalkulator

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% pengurangan token for its tested workload (internal benchmark). Lihat Kajian.

Kecekapan Flywheel: Segmentation β†’ Smart Routing β†’ Distillation β†’ Sistem Detached Sistem β†’ Infrastruktur Swasta. Semasa production for suitable workloads; larger-scale ambitions are Phase 2 / funding-dependent.

Kerangka ESG Token Study Download ESG Report
1 Segmentation→ 2 Smart Routing→ 3 Distillation→ 4 Sistem Berasingan→ 5 Peribadi Infra
Kalkulator

Kalkulator Jejak Karbon

Konfigurasikan parameter beban kerja anda dan bandingkan pemprosesan AI-Heavy berbanding NeuralOps Dioptimumkan.

Pratetap Pantas: Auto Β· Ainna NeuralOps
Keputusan

Papan Pemuka

Showing preset: Ainna NeuralOps Β· auto every 3s

Perbandingan Penggunaan Tenaga

Perbandingan Jejak Karbon

Pengagihan Penghalaan NeuralOps

Pengagihan Penghalaan Garis Dasar

Pengguna & Beban Kerja

Garis Dasar

AI-Heavy
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Jumlah: 100%

NeuralOps

Dioptimumkan
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Jumlah: 100%
Emulator

Pembina Senario

Cipta, simpan, dan bandingkan pelbagai senario beban kerja secara bersebelahan.

Senario Baharu

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Jumlah: 100%

Senario Tersimpan

Senario Permintaan Bulanan Tenaga (kWh) Karbon (kg COβ‚‚e) Kos Karbon Dijimatkan berbanding Garis Dasar Pengurangan %
Tiada senario disimpan lagi. Cipta satu di sebelah kiri.
ESG

The ESG logik 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 / Kecekapan 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 Sistem Berasingan→ 5 Peribadi Infra

02 / The operating levers

Three variables shape the projection below. Perubahan 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. Penghalaan simple work to rules, parsers, or lebih kecil 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.

Infrastruktur overhead

Infrastruktur 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 / Persekitaran

Persekitaran

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

Sosial

  • Make efficient AI more accessible to PKS
  • Support practical, understandable automation
G / Tadbir Urus

Tadbir Urus

  • 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

  • Tenaga Kecekapan and Conservation Act 2024
  • National Tenaga Transition Peta Hala Tuju (NETR)
  • Bursa Malaysia Kelestarian Reporting
Antarabangsa

Antarabangsa

  • IFRS S1 & S2 Kelestarian Piawaian
  • TCFD Β· GRI Β· SDGs
  • Paris Agreement climate objectives
Kerangka ESG β†’ Token Kecekapan Kajian β†’ Kecekapan Flywheel β†’ ESG Methodology Report (PDF) β†’
Pelajari

Bagaimana NeuralOps Mengurangkan Jejak Karbon

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 lebih kecil specialist that costs less energy to run when it is the right layer.

04

Sistem Berasingan

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

05

Infrastruktur Swasta

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

Perlindungan AI

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

08

ESG Pemantauan

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

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Penafian Ketelusan

Kalkulator ini menyediakan unjuran anggaran, 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% pengurangan token 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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