Green AI Orchestration Framework

AI that thinks about
the planet

GreenInfer routes every prompt to the most energy-efficient model capable of answering it - combining prompt optimization, complexity analysis, and carbon-aware routing into one unified inference layer.

Try GreenInfer How it works →
0%
Avg Energy Saved
0%
Token Reduction
0
Model Tiers
Open
Source Framework
How It Works

The Green Inference Pipeline

Every prompt passes through a multi-stage orchestration engine before a single GPU cycle is burned.

💬
User Input
Raw prompt submitted
→
✂️
Prompt Optimizer
Token compression & semantic preservation
→
🧠
Complexity Score
Classifies reasoning demand 0–100
→
⚡
Orchestrator
Routes to optimal model tier
→
🤖
Model Pool
Small / Medium / Large LLMs
→
🌿
Response + Carbon
Answer + energy metrics shown
Smart Routing

Right model.
Every prompt.

The orchestrator evaluates prompt complexity and accuracy requirements to assign the minimum viable model tier. Simple queries never touch large models.

🔓

Open Source Framework

Use GreenInfer in your own stack. Model-agnostic and developer-ready.

COMPLEXITY ROUTED TO ENERGY
Low · 12
SmolLM2 0.8 mWh
Low · 28
Phi-3 Mini 2.1 mWh
Med · 55
Mistral 7B 8.4 mWh
High · 78
Llama 3 70B 31.2 mWh
Max · 96
GPT-4 class 58.7 mWh
BASELINE (always large model) −73% avg
Core Features

Built for sustainable inference

🧬
Prompt Optimizer
Trained T5-based model compresses inputs 30–50%, removing redundancy while preserving full semantic meaning before any model call.
📊
Complexity Scoring
Linguistic entropy, token analysis, and task classification produce a 0–100 complexity score that guards against under-routing difficult prompts.
⚡
Sustainability Orchestrator
Core routing engine balances accuracy, latency, and real-time carbon intensity to pick the minimum viable model for each inference.
🌐
Carbon-Aware Routing
Connects to real-time Texas grid carbon data via ERCOT. When the grid is dirty, inference automatically shifts to smaller, lower-emission models.
🤖
Reinforcement Learning
RL policy learns from historical accuracy, energy, and latency metrics - continuously improving routing decisions over time without manual tuning.
🔌
Model-Agnostic
Works with any open-source or API model. Use GreenInfer as an inference layer for chatbots, code generation, summarization, or NLP pipelines.
Usage Modes

You choose how green

🌿
ECO MODE

Maximum Green

Always routes to the smallest viable model. Carbon budget enforced. Best for simple queries and low-stakes tasks.

⚖️
BALANCED

Smart Default

Dynamically weighs accuracy needs vs energy cost. Grid-aware. The recommended mode for most users.

🚀
PERFORMANCE

Full Capability

Accuracy first - still energy-tracked and reported. Uses large models when needed. Eco gains from prompt optimization only.

Get Started

Try it. Track it. Save it.

Open GreenInfer, ask a question, and watch your carbon footprint in real time. Every session saves energy. Every prompt tracked.

Open Chat Interface Read the Framework Docs