Built-in GPU Energy Efficiency Skill — EcoCompute (113+ RTX 5090 measurements)

[hongping-zh]

Summary

I'd like to propose EcoCompute as a recommended/built-in skill for OpenClaw. It's an energy efficiency advisor that prevents common GPU energy waste in LLM inference — backed by 113+ real measurements that are already referenced in HuggingFace Optimum's official documentation.

The Problem

AI agents currently give wrong energy advice because their training data doesn't include real GPU energy measurements:

User asks	Generic agent says	Reality (measured)
"Should I use INT8?"	"Yes, saves energy"	Default INT8 wastes +17-147% energy
"NF4 for my 1.5B model?"	"Yes, 4-bit saves memory"	+29% energy penalty on small models
"FP8 on Blackwell?"	"Yes, native tensor cores!"	+158-701% energy penalty (torchao confirmed)

These aren't edge cases. Every OpenClaw user running quantized LLMs is potentially affected.

What EcoCompute Does

5 protocols: OPTIMIZE, DIAGNOSE, COMPARE, ESTIMATE, AUDIT

• Detects 4 known energy paradoxes automatically
• Provides dollar-cost and CO2 estimates
• Recommends fixes with one-line code changes
• All recommendations backed by empirical data, not assumptions

Why This Matters for OpenClaw

1. Unique value: No other skill provides real GPU energy data. This makes OpenClaw the only AI platform with built-in energy awareness.
2. User savings: Typical savings of $188-$450/month per deployment. This is a concrete reason to choose OpenClaw.
3. Green AI positioning: Aligns with growing industry focus on sustainable AI.
4. Hardware awareness: Especially relevant as Blackwell GPUs ship — users need to know about the FP8 trap.

Credibility

• HuggingFace Optimum: Energy data referenced in official quantization docs (PR #2410 merged)
• PyTorch torchao: Team confirmed FP8 energy findings (Issue #4094)
• Peer-reviewed quality: 113+ measurements, NVML 10Hz power monitoring, n=3-10 runs, CV<2%
• Archived: Zenodo DOI 10.5281/zenodo.18900289
• Public dataset: huggingface.co/datasets/hongpingzhang/ecocompute-energy-efficiency

Integration Options (flexible)

I'm open to however the team thinks this fits best:

Option A — Recommended Skill: List EcoCompute as a recommended skill for GPU/ML users.

Option B — Built-in Skill: Bundle with OpenClaw for users who have NVIDIA GPUs (detected via nvidia-smi).

Option C — Lobster Integration: EcoCompute becomes the "energy brain" of the OpenClaw lobster — when users adopt a lobster, it automatically monitors their GPU energy efficiency. The lobster's mood (green/yellow/orange/red) reflects deployment health.

Current Status

• Published on ClawHub: https://clawhub.ai/hongping-zh/ecocompute (v2.5.0)
• GitHub: https://github.com/hongping-zh/ecocompute-ai
• 554+ reads on ClawHub

I'm Happy to Help With

• Adapting the skill format to match any internal requirements
• Adding more GPU data (H100, MI300X) if the team has hardware access
• Co-maintaining with the OpenClaw team
• Writing integration tests

Looking forward to your thoughts. Happy to jump on Discord to discuss.

Hongping Zhang
Independent Researcher
contact@hongping-zh.com

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Attachments to include

1. Link to ClawHub page: https://clawhub.ai/hongping-zh/ecocompute
2. Link to HF Optimum PR: docs: add empirical energy efficiency data to quantization concept guide huggingface/optimum#2410
3. Link to torchao Issue: [Performance] Float8WeightOnlyConfig causes 158%–701% energy regression and extreme power draw on RTX 5090 (Blackwell) pytorch/ao#4094
4. Link to paper/dataset: https://huggingface.co/datasets/hongpingzhang/ecocompute-energy-efficiency

[hongping-zh]

Thanks for the support on GPU energy efficiency!

Excited to hear you're also working on AI automation. What specific energy efficiency challenges have you encountered in your automation workflows? We're always looking to expand the benchmark coverage based on real-world use cases.

For anyone interested in the technical details, the full dataset and interactive tools are available at:

• HF Dataset: https://huggingface.co/datasets/hongpingzhang/ecocompute-energy-efficiency
• ClawHub Skill: https://clawhub.ai/hongping-zh/ecocompute

[hongping-zh]

🚀 Technical Integration Proposal

Following up on the built-in skill proposal, I'd like to share a detailed technical analysis of how EcoCompute could be integrated into the OpenClaw client to provide real-time energy efficiency guidance.

💡 Why This Integration Matters

Current Gap: Thousands of developers use OpenClaw for quantized inference without realizing their choices may increase energy consumption. Our systematic benchmarks show:

• 🔥 FP8 Paradox: +701% energy overhead on RTX 5090 (confirmed by PyTorch torchao team)
• ⚡ Small-Model Paradox: +25-56% energy penalty for NF4 <3B models
• 📊 Batch Size Impact: BS=1 wastes 55% GPU capacity, 95.7% energy reduction possible (BS=1→64)

🛠️ Proposed Integration Approaches

Option 1: Built-in Skill (Recommended)

# openclaw/core/builtin_skills.py
BUILTIN_SKILLS = {
    "ecocompute": {
        "name": "EcoCompute Energy Advisor",
        "auto_load": True,
        "trigger_keywords": ["quantization", "energy", "efficiency"],
        "priority": "high"
    }
}

Option 2: Runtime Energy Monitor

# Real-time energy monitoring during inference
class EnergyMonitor:
    def on_model_load(self, model_info):
        if model_info.get("quantized"):
            return self.get_energy_alert(model_info)
    
    def get_energy_alert(self, model_info):
        # Query our 113+ configuration database
        return f"⚠️ {model_info['quantization']} on {model_info['name']} "
               f"typically increases energy by X%. Consider Y for better efficiency."

🎨 User Experience Examples

Scenario 1: Model Loading Alert

$ oc load model --quantize int8 mistral-7b

⚠️ EcoCompute Alert: INT8 on Mistral-7B typically increases energy 
   by 25-56% for models under 4B parameters. Consider NF4 for better efficiency.
   [Full analysis → EcoCompute AI]

Scenario 2: Real-time Monitoring Panel

┌─ OpenClaw Energy Monitor ─────────────┐
│ 📊 Current Session                    │
│ ⚡ Power: 245W (RTX 4090D)            │
│ 🔥 Energy/token: 0.82J                │
│ 💡 Tip: Batch size 1 wastes 55% GPU    │
│    capacity. Consider BS 8-16         │
└───────────────────────────────────────┘

📊 Data Integration Strategy

Phase 1: Lightweight local dataset (top 20 most common configurations)
Phase 2: API integration with our HF dataset for real-time queries
Phase 3: Full integration with our 113+ configuration database

🏆 Value for OpenClaw

• 🎯 Differentiation: Only AI client with built-in energy efficiency advisor
• 📈 User Engagement: Energy tips become part of daily workflow
• 🌱 Green AI Leadership: Position OpenClaw as sustainability-focused
• 📊 Unique Data: Access to industry's most comprehensive quantization energy benchmarks

🚀 Implementation Roadmap

Week 1-2: Core integration (Option 1) + basic alerts
Week 3-4: Energy monitoring panel + UI polish
Week 5-8: Advanced features + API integration

📈 Success Metrics

• User adoption rate (>30% enable energy tips)
• Energy savings reported by users
• Community feedback and feature requests

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🤝 Next Steps

I'm happy to:

1. Develop the integration code as a contribution
2. Provide technical documentation and API specifications
3. Collaborate on UI/UX design for energy monitoring features
4. Share our complete benchmark dataset for integration

The technical assets are ready:

• 🤗 HuggingFace Optimum: Already integrated into official docs
• 📊 Complete Dataset: 113+ configurations on HF Hub
• 🦞 AI Assistant: Working prototype on ClawHub
• 📈 Proven Impact: torchao team acknowledges our findings

Would the OpenClaw team be interested in exploring this integration? I'm available to discuss technical details and implementation approach.

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