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Edge AI

Edge AI & Embedded GPU Fleets

Jetson thermal budget power envelope inference FPS

How Graphite achieves this
Lightweight Graphite plaintext protocol runs on Jetson Orin/AGX, metrics forwarded over HTTPS without a local TSDB or metrics server on the device
Device serial and deployment batch encoded in Graphite paths, fleet-wide and per-device thermal dashboards using wildcard and groupByTags queries
Power envelope alerting: Graphite threshold fires when a device exceeds its thermal design power budget, catches throttle risk before inference degrades
Model update regression tracking: Graphite compares inference FPS distributions before and after deployment using timeShift(), fleet-wide validation in one dashboard
MetricFire includes pre-built Grafana dashboards for edge AI fleets. Per-device thermal, power envelope, inference FPS, and thermal margin panels, ready on day one with no dashboard configuration needed
Graphite metrics collected
edge.{id}.gpu_util_pct edge.{id}.gpu_temp_c edge.{id}.power_mw edge.{id}.memory_used_mb edge.{id}.inference_fps edge.{id}.model_latency_ms edge.{id}.thermal_margin_c
Self-hosted pain solved
Can't ship a full monitoring stack to each edge device → Graphite plaintext protocol is a 200-byte per-metric UDP/TCP send
No fleet-level visibility from distributed edge → Graphite wildcard paths aggregate thousands of devices in one query
Model update regression undetectable without fleet-wide FPS tracking → Graphite timeShift() compares before/after distributions across all devices
Graphite value: Edge AI teams get remote fleet-wide GPU observability without shipping a monitoring stack to each device. One Graphite instance receives metrics from thousands of edge units, visualised in real-time MetricFire-hosted Grafana dashboards with zero device-side infrastructure.

GPU Monitoring Use Cases
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MetricFire's Hosted Graphite covers every GPU workload. See how it fits your team's specific challenge.