Ecocompute
Skill by hongping-zh
Why use this skill?
Optimize your LLM inference energy usage with EcoCompute. Get empirical data-backed recommendations for GPU hardware, quantization, and batching strategies.
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/hongping-zh/ecocomputeWhat This Skill Does
EcoCompute is a specialized OpenClaw AI agent skill designed to act as an energy efficiency advisor for Large Language Model (LLM) inference. Powered by empirical data from 93+ measurements across diverse hardware architectures like NVIDIA's Blackwell (RTX 5090), Ada Lovelace (RTX 4090D), and Ampere (A800), this skill provides actionable, data-driven recommendations to minimize the environmental and operational cost of your AI deployments. Instead of relying on guesswork, EcoCompute calculates the energy footprint of your inference pipeline by analyzing model parameters, hardware constraints, quantization methods, and batch sizes. It helps developers move from wasteful, unoptimized setups to high-efficiency configurations by balancing performance with power consumption.
Installation
To integrate EcoCompute into your OpenClaw environment, execute the following command in your terminal:
clawhub install openclaw/skills/skills/hongping-zh/ecocompute
Ensure you have the latest version of the OpenClaw CLI installed before running the installation command.
Use Cases
- Cloud Cost Optimization: Reduce electricity expenses by selecting the most efficient hardware and quantization combination for your specific model size.
- Hardware Selection: Determine whether to scale horizontally on consumer-grade GPUs or utilize enterprise-grade A800 units based on predicted power draws.
- Production Deployment Planning: Simulate the power impact of different concurrency levels (batch sizes) to ensure your API remains within green energy thresholds or power budgets.
- Quantization Impact Assessment: Evaluate if shifting from FP16 to NF4 significantly degrades performance compared to the energy savings gained.
Example Prompts
- "Analyze the energy consumption of running Llama-3-8B on an RTX 4090D with a batch size of 8 and fp16 quantization."
- "I am planning to deploy Mistral-7B-Instruct. Which quantization method offers the best balance of speed and power consumption on an A800 GPU?"
- "Evaluate my deployment config: model=Qwen2-72B, hardware=h100, batch_size=32, quantization=int8_pure. Will this exceed standard thermal limits?"
Tips & Limitations
- Hardware Matching: Always specify the GPU platform accurately; the power profiles of an RTX 5090 and an A100 are vastly different due to architecture and cooling constraints.
- Default Parameters: If you are unsure, stick to the provided defaults (FP16 quantization, BS=1), but be aware that running a batch size of 1 in production is rarely energy efficient due to poor GPU utilization.
- Data Limitations: While the tool draws from extensive empirical data, real-world power draw may fluctuate based on specific cooling solutions, power limit settings (TDP), and ambient temperature of the server room.
Metadata
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Find the right skillPaste this into your clawhub.json to enable this plugin.
{
"plugins": {
"official-hongping-zh-ecocompute": {
"enabled": true,
"auto_update": true
}
}
}