Know What’s Changing Before It Becomes an Outage
Modern cloud environments generate signals everywhere. QuietMetric brings those signals together, helps your team understand unusual system behaviour, and highlights potential operational risks before they become bigger problems.
See what’s happening. Understand what’s changing. Act earlier.
Evidence-based reliability intelligence for AI infrastructure
As AI infrastructure scales from single clusters to distributed GPU superpods, traditional monitoring tools generate cascading alerts across fragmented systems — GPU hardware, inference runtimes, orchestration layers, and network fabrics — without the contextual intelligence to distinguish root causes from downstream symptoms.
QuietMetric addresses this challenge by connecting the signals that matter across accelerators, runtimes, and cloud infrastructure, delivering causal traceability and noise suppression that turns chaotic telemetry into accountable operator action. The platform continuously profiles GPU fleet health — monitoring SM clock, memory bandwidth, NVLink throughput, ECC error rates, and thermal throttling — while correlating these hardware signals with inference server metrics, model serving performance, and distributed tracing data.
By applying GPU-accelerated anomaly detection and digital fingerprinting, QuietMetric learns normal behavior profiles for every component in the AI stack and surfaces deviations before they impact production workloads.
Compute Requirement
The platform architecture requires massive accelerated computing capabilities to process high-bandwidth telemetry streams from thousands of GPUs, run complex graph-based causal correlation models, execute real-time anomaly detection, and deliver low-latency observability intelligence across distributed AI data centers.
Unified Evidence Timelines
Correlates GPU-level, inference-level, and network-level signals into a single accountable timeline, replacing fragmented alert streams.
Causal Traceability
Collapses hundreds of downstream alerts into unified root-cause timelines so teams investigate with evidence rather than speculation.
Continuous Fleet Profiling
Tracks SM clock, memory bandwidth, NVLink throughput, ECC error rates, and thermal throttling across the GPU fleet.
Operator Sovereignty
Every recommendation carries confidence scores and audit logging, separating observed evidence from inferred hypotheses with strict uncertainty bounds.
Predictive, Not Reckless
Anticipates failures, recommends capacity adjustments, and automates routine optimization — always with human review and rollback safety.
Real-Time Ingest
High-frequency telemetry from DCGM, Triton metrics endpoints, Prometheus, OpenTelemetry collectors, and custom instrumentation.
Technical stack
QuietMetric follows a modern AI-native architecture designed for real-time telemetry processing, predictive analytics, and infrastructure observability workloads.
Languages & Runtime
- • Python
- • PyTorch
- • ONNX Runtime
- • React
Core application and model runtime surface.
Real-Time & Streaming
- • FastAPI & WebSockets
- • Apache Kafka
- • OpenTelemetry
- • eBPF-based network tracing
Sub-second ingest and transport layer.
Data & Storage
- • PostgreSQL & TimescaleDB
- • Vector Database Infrastructure
- • Redis
- • Apache Spark
Time-series, vector, and analytical persistence.
Platform & Ops
- • Docker
- • Kubernetes (Amazon EKS)
- • MLflow
- • Prometheus & Grafana
Deployment, lifecycle, and observability control plane.
Current NVIDIA Integration Status
QuietMetric is currently architecting its AI infrastructure for NVIDIA accelerated computing. NVIDIA technologies are critical to the production roadmap, particularly for enabling deep GPU telemetry collection via DCGM, real-time inference server observability through Triton, GPU-accelerated anomaly detection with Morpheus, fabric-level observability with NetQ, and high-performance telemetry analytics with RAPIDS. The platform leverages NVIDIA's comprehensive observability ecosystem to bring GPU-accelerated infrastructure intelligence to enterprise AI operations.
Planned architecture
QuietMetric requires a comprehensive NVIDIA technology stack focused on deep GPU telemetry, inference observability, real-time anomaly detection, and predictive operations intelligence.
Purpose: Deep GPU telemetry collection and health monitoring across the AI infrastructure fabric.
Planned Implementation
- Per-GPU Hardware Metrics: SM clock, memory bandwidth, NVLink throughput, ECC error rates, temperature, power usage, and PCIe state.
- Continuous GPU Telemetry: Low-overhead telemetry collection associating utilization, energy, and error with scheduler jobs.
- Online Diagnostics: Passive background monitoring and readiness checks before work is admitted.
- Fabric & Adapter Observability: NVSwitch systems discovery and port link state tracking.
- Management Policies: Threshold watch notifying clients and triggering recovery actions.
- Prometheus Integration: DCGM Exporter exposing GPU metrics for QuietMetric telemetry pipelines.
Expected Benefits
Purpose: Real-time inference server metrics and model serving observability.
Planned Implementation
- Inference Request Metrics: Count, latency, queue time, and compute time for successful and failed requests.
- GPU Metrics: Utilization, used memory, free memory, and power usage.
- Model Version Statistics: Performance metrics per model version for A/B testing and rollback.
- Prometheus & OTel: Metrics exposure via port 8002 in OpenTelemetry compatible format.
Expected Benefits
Purpose: AI-powered anomaly detection and predictive intelligence for infrastructure telemetry.
Planned Implementation
- Real-Time Anomaly Detection: GPU-accelerated inference on high-velocity GPU telemetry streams.
- Digital Fingerprinting: Learning normal profiles of system components to surface deviations.
- Graph-Based Context: Pre-trained Graph Neural Networks for contextual analysis.
- Root Cause Analysis: Binary sequence classification on kernel logs and traces.
Expected Benefits
Purpose: GPU-accelerated telemetry data processing, analytics, and graph-based correlation.
Planned Implementation
- cuDF: Accelerate real-time telemetry processing across high-bandwidth streams.
- cuGraph: Graph analytics for causal correlation and dependency mapping.
- cuML: Predictive failure modeling on operational data.
- Apache Spark: Scalable batch analytics for large historical telemetry datasets.
Expected Benefits
Purpose: Framework-agnostic observability agents for infrastructure monitoring and debugging.
Planned Implementation
- Unified Observability: Standardized agent instrumentation across heterogeneous AI stacks.
- Distributed Tracing via Phoenix: Trace-driven visibility into agent and pipeline execution paths.
- NeMo Intake: Trace-driven diagnosis workflows for continuous improvement.
- OpenTelemetry: Interoperable telemetry export into QuietMetric's evidence timelines.
Expected Benefits
Purpose: Holistic network fabric observability for AI data center networking.
Planned Implementation
- Real-Time Visibility: Troubleshooting and validation of NVIDIA NVLink switches and Cumulus fabrics.
- Streaming Telemetry: Native support for data-plane anomaly detection.
Expected Benefits
Additional NVIDIA Integrations
Optimize anomaly detection and GNN models for production inference with INT8/FP16 quantization.
Serve observability models concurrently with dynamic batching and ensemble pipelines.
Production-ready optimized inference microservices with OpenTelemetry metrics and traces export.
Production-ready AI software, STIG-hardened containers, FIPS-enabled deployments, and enterprise support.
Justification on AWS infrastructure
QuietMetric is designed to process high-volume GPU telemetry from thousands of accelerators while running complex AI models for anomaly detection, causal correlation, and predictive operations.
Technical Justification — Amazon EC2 P4d
NVIDIA A100 Tensor Core GPUsThe platform workloads include real-time GPU telemetry processing at scale, anomaly detection modeling, graph-based causal correlation across heterogeneous signals, and continuous model training from operational data. NVIDIA H100 and A100 Tensor Core GPUs provide the computational performance required for training complex anomaly detection models, processing massive telemetry streams, and running real-time graph-based correlation. Access to AWS GPU credits through the NVIDIA Inception Program will allow QuietMetric to validate its observability algorithms on production-relevant hardware.
Q3 – Q4 execution plan
Fourteen milestones spanning telemetry collection, GPU-accelerated analytics, model optimization, and enterprise-grade deployment.
Reviewer credentials
Access details for evaluation environments. Credential values are redacted in this profile and are supplied through NVIDIA's secure Inception submission channel.
Environment Logins
Prepared for the NVIDIA Inception Program
This technical application profile has been prepared for submission to the NVIDIA Inception Program.