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Energy Intelligence for AI Infrastructure
GPU-level power monitoring mapped to jobs, models, and teams.
AluminatiAI makes AI energy usage transparent, attributable, and optimizable—so you can reduce waste, control costs, and build energy-aware infrastructure without slowing innovation.
Our Mission
AluminatiAI's mission is to make AI energy usage transparent, attributable, and optimizable. As AI workloads scale, energy becomes a hidden constraint on cost, performance, and sustainability. We give AI teams precise visibility into GPU-level power consumption—mapped to jobs, models, and teams—so they can reduce waste, control costs, and build energy-aware AI infrastructure without slowing innovation.
The Problem
AI Energy Usage Is Opaque
Most AI teams have no visibility into how much energy their workloads consume. GPUs draw hundreds of watts per card, but this data is rarely collected, let alone attributed to specific jobs or models.
Cloud Bills Don't Tell the Story
Utilization metrics show when GPUs are busy, but not how much power they consume. Cloud invoices show instance costs, but hide the energy footprint. Without attribution, optimization is guesswork.
Scale Amplifies the Gap
As teams move from a few GPUs to hundreds or thousands, energy becomes a real constraint—on budgets, on datacenter capacity, and on sustainability commitments.
Compliance Lacks Technical Depth
Sustainability reporting requires accurate energy data. Generic carbon calculators don't understand AI workloads. You need metrics that map to real infrastructure.
How AluminatiAI Solves This
GPU-Level Power Monitoring
We capture real-time power draw, utilization, and thermal data directly from GPUs using lightweight agents that don't disrupt your workloads.
Job-, Model-, and Team-Level Attribution
Energy usage is mapped to the jobs that consumed it—not just the instance or cluster. See which models, experiments, or teams are driving your energy footprint.
Optimization Insights That Matter
Compare training runs. Identify inefficient jobs. Make energy-aware scheduling decisions. Reduce waste without sacrificing performance.
How It Works
Collect
Lightweight agents deployed on your infrastructure capture GPU power consumption, utilization, temperature, and clock speed—without interfering with training or inference workloads.
Attribute
Energy metrics are mapped to specific jobs, models, users, and teams. Not just "this GPU used 300W"—but "this training run consumed 15 kWh over 6 hours."
Optimize
Identify inefficient jobs, compare experiment energy costs, and make data-driven decisions about scheduling, hardware selection, and infrastructure capacity.
Start Monitoring Your GPUs Today
Try our lightweight GPU monitoring agent free for 30 days. Track energy costs, identify waste, and optimize your infrastructure—all from a real-time dashboard.
Lightweight
Less than 1% CPU overhead and 100MB RAM. Deploy in seconds with a single install script.
Real-Time Dashboard
See your GPU energy costs live. Track power draw, utilization, and identify inefficient jobs instantly.
Your Data
Metrics stay in your infrastructure. Full control over retention, access, and privacy.
No credit card required • 30-day free trial • Install in under 5 minutes
Built Specifically for AI Workloads
Energy-first monitoring. Traditional tools focus on utilization or throughput. We start with power consumption and work backwards to attribution and optimization.
Designed for ML infrastructure. Not generic compute monitoring adapted for AI. Built from the ground up to understand training runs, inference workloads, and multi-GPU jobs.
Attribution at every layer. From the GPU to the model to the team. Energy usage becomes a first-class metric alongside accuracy, latency, and cost.
The Future of AI Is Energy-Aware
As AI scales, teams that understand and optimize their energy footprint will build faster, cheaper, and more sustainable infrastructure.
Join ML platform teams and AI infrastructure engineers building the next generation of energy-aware systems.