𝐖𝐡𝐲 𝐆𝐏𝐔 𝐌𝐞𝐭𝐫𝐢𝐜𝐬 𝐌𝐚𝐭𝐭𝐞𝐫: 𝐈𝐧𝐬𝐢𝐝𝐞 𝐁𝐓𝐓𝐈𝐧𝐟𝐞𝐫𝐆𝐫𝐢𝐝'𝐬 𝐃𝐂𝐆𝐌 𝐄𝐱𝐩𝐨𝐫𝐭𝐞𝐫 How do you monitor a GPU if all you know is that the server is online? You need visibility into the hardware itself. BTTInferGrid's documented miner deployment includes "dcgm-exporter", which exposes NVIDIA GPU metrics through a "/metrics" endpoint. How does it fit into the deployment? 1. Collect GPU Metrics The exporter makes GPU monitoring data available to other components. 2. Expose a Metrics Endpoint The documented default endpoint is "http://dcgm-exporter:9400/metrics" within the deployment network. 3. Connect Metrics to Node Operations The miner agent reads GPU metrics from the exporter and collects additional information about the host and inference runtime. Why this matters Hardware monitoring and application monitoring answer different questions. GPU metrics provide visibility into the computing resources, while runtime checks help establish whether the model-serving process is responding. Combining these signals gives operators a more complete picture of the node. However, metrics alone don't prove that a GPU is delivering correct inference results or that every workload can be served successfully. BTTInferGrid's architecture makes GPU visibility part of the miner-side operational stack. Because you can't manage computing resources effectively if you can't observe their condition. @BitTorrent @justinsuntron #BitTorrent #BTTInferGrid #DeAI #GPUComputing #TRONEcoStar