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Invisible Waste
Most datacenters do not know which GPUs are idle, underutilized, or running inefficiently. Without tensor-core level visibility, you are guessing where the problems are.
Infrastructure Intelligence by Ureka
Make Your Infrastructure Think.
Omniference is the intelligence layer that transforms AI infrastructure from reactive to predictive, optimizing every workload, rack, and gigawatt through continuous learning.
Infrastructure Overview
● SYSTEM HEALTHYGPU Utilization87.4%↑ 12.8%
Power Efficiency1.18 PUE↑ optimized
Cost / 1M Tokens$0.42↓ 28.2%
Active GPUs4,09699.8% online
GPU UTILIZATION BY CLUSTER
EFFICIENCY TREND
Estimated Monthly Savings$384,200
Prevented SLA Events143
Energy Saved1.84 GWh
Illustrative dashboard — sample data, not live customer metrics.
Live Infrastructure Twin
See the datacenter as a living system.
Streaming telemetry connects GPU behavior, network pressure, cooling flow, and power draw in one operational view.
DATACENTER FLOOR · ZONE ALIVE TELEMETRY
4,096 accelerators onlinePower, thermal, fabric and workload signals correlated in real time — illustrative view
TELEMETRY STREAMLast 60 seconds
GPU COMPUTE PRESSURE87.4%
Stable throughput
82%
SM ACTIVE74%
HBM BW61%
NVLINKTHERMAL ENVELOPE22.8°C supply
1.18LIVE PUE
31 kWRACK AVG
0.7%THROTTLE RISK
99.8%FLEET HEALTH
Deployment Intelligence
From model arrival to the best rack, automatically.
Omniference evaluates latency, memory, power, fabric locality, cooling headroom, and cost before placing each workload.
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Policy-aware placementSLA, security, locality and cost constraints applied before deployment.
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Shadow optimizationCandidate changes are validated against projected impact before promotion.
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Continuous rebalancingWorkloads shift as demand, thermal limits and infrastructure health change.
GLOBAL DEPLOYMENT GRAPHOPTIMIZING
US-WEST · H10038% headroom · 1.16 PUE
US-CENTRAL · B20061% headroom · low latency
EU-NORTH · MI300Xrenewable window active
EDGE CLUSTERbatch-1 inference ready
OmniferencePlacement Brain
Candidate deployment simulatedProjected: 28% lower cost · 19% lower energy · SLA maintained — illustrative scenario
Pain Points
AI infrastructure hides cost and complexity.
AI infrastructure presents challenges in terms of cost and transparency. Omniference exposes what conventional dashboards miss.
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Reactive Operations
Infrastructure teams respond to failures after they happen. By the time you see a bottleneck, it has already cost money, time, and SLA violations.
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Optimization Theatre
Manual tuning cannot keep up with dynamic workloads. Static configurations leave potential performance and efficiency on the table.
KPIs
Target outcome
↑ 40%
GPU Utilization
Target outcome
↓ 25%
Power Consumption
Target outcome
↓ 30%
Infrastructure Cost
Target outcome
↑ 35%
Sustainability
These represent optimization goals based on our research. Actual results vary by infrastructure.
Our Approach
A closed-loop intelligence layer.
Omniference combines real-time telemetry with adaptive optimization. Instead of reacting to problems, your infrastructure predicts and prevents them.
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Self-Aware
Every tensor core, rack, and workload monitored continuously through a unified infrastructure model.
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Self-Learning
ML-driven models learn from projected and observed performance, improving optimization over time.
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Self-Optimizing
Automatic recommendations and policy-aware adjustments without constant manual intervention.
How It Works
Observe. Predict. Optimize. Learn.
Model
Transform AI workloads into operator graphs for efficient processing.
Simulate
Predict performance, cost, and energy impact before changing production systems.
Measure
Collect live telemetry from GPUs to racks and datacenter systems.
Correlate
Identify drift between projected and observed infrastructure performance.
Optimize & Learn
Recommend corrective actions and refine models with every cycle.
From Tensor Core to Gigawatt
One intelligence plane, every infrastructure layer.
Omniference operates at micro and macro levels simultaneously, connecting workload behavior to datacenter economics.
Micro-Level Optimization
Tensor Core to GPU
✓Operator-level profiling and kernel tuning
✓Quantization and precision management
✓KV-cache optimization for inference
✓Memory, compute, and interconnect correlation
Macro-Level Intelligence
Rack to Datacenter
✓Cross-rack workload scheduling
✓Power envelope management
✓Cooling zone optimization
✓Capacity, cost, and sustainability planning
Omniference by Ureka
Make Your Infrastructure Think.
Turn fragmented telemetry into predictive intelligence and continuous infrastructure optimization.