模型 大小
Larger models generally require more compute and memory per request. 路由 simple work to rules, parsers, or smaller specialists can avoid unnecessary model load.
估算 how 智能路由, rule-based processing, detached 系统, and GPU-only-when-needed architecture can reduce digital carbon footprint. A controlled study estimated up to 87% 令牌降耗 for its tested workload (内部基准). 查看研究.
效率飞轮: 分段 → 智能路由 → 蒸馏 → 分离式系统 → 专用基础设施. 当前 production 适用于合适的工作负载; larger-scale ambitions are 第二阶段 / funding-dependent.
Configure your workload 参数 and compare AI-重型 vs NeuralOps 已优化 processing.
Showing preset: Ainna NeuralOps · auto every 3s
创建、保存并并排比较多个工作负载场景。
| 场景 | Monthly Requests | 能源 (kWh) | 碳 (kg CO₂e) | 成本 | 碳 Saved vs 基线 | 减排量 % | |
|---|---|---|---|---|---|---|---|
| 尚未保存任何场景。请在左侧创建一个。 | |||||||
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.
The model starts with a simple principle: use the least intensive layer that can complete the task. 分段 and routing happen first; detached 工作流 remove repeat work; controlled infrastructure makes the remaining compute easier 来观测. 蒸馏 at scale and larger clusters remain 第二阶段 / funding-dependent.
Three variables shape the projection below. 更改 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.
Larger models generally require more compute and memory per request. 路由 simple work to rules, parsers, or smaller specialists can avoid unnecessary model load.
Repeated requests multiply energy use. Caching and detached 系统 can complete predictable work without sending the same job through a model again.
数据-centre overhead matters. The PUE field scales the energy estimate so infrastructure efficiency remains visible alongside the routing mix.
These are 设计 commitments, not proof of certified ESG performance.
The framework is intended to support conversations about energy efficiency, disclosure, and responsible technology 设计. It does not replace legal, accounting, lifecycle, or assurance advice.
NeuralOps is AINNA's orchestration layer. It sends each task to the smallest sufficient 系统 — rules, parsers, detached 工作流, a distilled specialist, or a GPU model only when reasoning is required. That is the ESG argument modelled in this calculator.
将进入的工作拆分为有界任务，这样就不会为一个小步骤启动整个 GPU 作业。 Less wasted inference before routing even begins.
NeuralOps routes to the cheapest layer that can do the job: rules first, then parser, GPU last.
Move repeated capability from a large model into a smaller specialist that costs less energy to run when it is the right layer.
可重复工作流s run as deterministic services without an LLM in the loop for the same job twice. How 分离式系统 work →
Controlled, observed compute stays on infrastructure you can account for. Larger clusters are 第二阶段 / funding-dependent.
Structured input is extracted and validated by rule-based services. AI is a fallback.
停止浪费的重试、过大的提示词，以及消耗令牌却不改变结果的循环。
监视 where work ran — rules, detached 系统, or model — so energy use can be reviewed, not guessed.
此计算器提供 估算预测, not a certified carbon audit. Final carbon footprint should be 已验证 using actual infrastructure logs, cloud usage reports, model runtime data, regional grid emission factors, and data centre energy metrics. The 87% 令牌降耗 figure is an 内部基准 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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