IOTAIRx slashes AI Training Costs and Carbon Footprint by 48% with Pebble's EcoAgent
Healthcare AI Company IOTAIRx faced significant infrastructure costs and energy consumption while training their advanced AI models. By deploying Pebble's EcoAgent for a long-running fine-tuning job, IOTAIRx achieved a 40% reduction in compute and infrastructure costs and a corresponding 48% reduction in energy consumption and carbon emissions, demonstrating a powerful new path to sustainable and cost-effective AI development.
IMPACT:
40% | 47% |
Savings in Infrastructure Cost1 | Reduction in power consumption and carbon emissions. |
Customer: IOTAIRx
IOTAIRx is a cutting-edge healthcare AI company dedicated to revolutionizing frontline diagnostics. Their mission is to build an AI co-pilot for generalist physicians, empowering them with differentiated diagnostic reasoning to dramatically reduce the time to specialist referrals and improve patient outcomes.
To achieve this, IOTAIRx trains and fine-tunes sophisticated AI models, a process that is both computationally intensive and expensive.
The Challenge: Unsustainable AI Training Costs
The IOTAIRx team was running a critical fine-tuning job on a deepseek-ai/DeepSeek-R1-Distill-Qwen-32B model using a custom medical dataset. Their infrastructure, built on Google Cloud Platform (GCP), utilized a Google Kubernetes Engine (GKE) cluster in `us-central1` region powered by NVIDIA A100 GPUs—a setup known for its high performance but also its significant energy and cost footprint. As the company scaled its operations, the spiraling costs and environmental impact of these long-running training jobs became a primary concern.
The Solution: Pebble's EcoAgent
Pebble's team engaged with IOTAIRx to deploy EcoAgent, an energy and carbon-aware intelligent software solution designed to optimize cloud and GPU resources. EcoAgent dynamically manages power consumption and resource allocation during AI/ML workloads without compromising performance.
The solution was applied to a representative fine-tuning job to quantify its impact. The training process was divided into 20 epochs: half of them ran without EcoAgent to establish a baseline, and the other half ran with EcoAgent enabled.
The Results: A 48% Reduction in Energy Consumption and Carbon Emissions
The impact of Pebble's EcoAgent was immediate and substantial.
Once activated at the start of an epoch, EcoAgent optimized the training job's power draw and compute utilization. This resulted in a 40% reduction in infrastructure costs for the duration of the job.
The energy and environmental savings were equally impressive. The total power consumed by the GPU cluster and the associated carbon emissions (measured in lbs of CO2) dropped by 48%.
The chart below visualizes the dramatic difference in performance.
As shown, with EcoAgent disabled, both power consumption and carbon emissions quickly soared. However, the moment EcoAgent was enabled for the second epoch, both metrics plummeted and stabilized at a significantly lower level for the remainder of the training job.
Conclusion: Sustainable AI is the way forward
By integrating Pebble's EcoAgent, IOTAIRx successfully addressed one of the most pressing challenges in the AI industry: the high cost and environmental impact of AI model training. The 48% savings in both cost and carbon emissions provides IOTAIRx with a significant competitive advantage, allowing them to allocate resources more efficiently, accelerate their development cycles, and pursue their mission in a more sustainable and financially responsible manner. This case study demonstrates that optimizing for environmental impact is not just an ethical choice, but a strategic business decision that delivers a powerful return on investment.
1 Based on per hour on-demand pricing for the Accelerator-optimized VM on GCP in the us-central1 region.
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