𝐅𝐫𝐨𝐦 𝐙𝐞𝐫𝐨 𝐭𝐨 𝐀𝐈 𝐈𝐧𝐟𝐞𝐫𝐞𝐧𝐜𝐞: 𝐓𝐡𝐞 𝐁𝐓𝐓𝐈𝐧𝐟𝐞𝐫𝐆𝐫𝐢𝐝 𝐌𝐢𝐧𝐞𝐫 𝐒𝐞𝐭𝐮𝐩 𝐅𝐥𝐨𝐰 Deploying an AI model on a GPU server involves several moving parts. You need a compatible host, a working container environment, a configured inference runtime, and a way to check whether the service starts correctly. BTTInferGrid's Quick Start documentation organizes this process into a repeatable workflow. 1. Prepare the Host The documented requirements include Linux x86_64, working NVIDIA drivers, Docker, Docker Compose, NVIDIA Container Toolkit, and Python 3.10 or later. 2. Generate the Configuration Use "miner-cli init" to create a starter YAML file specifying the model, inference engine, GPU configuration, and API port. 3. Validate the Environment Run "miner-cli doctor" and verify GPU container support before preparing the runtime. 4. Prepare and Launch Use "miner-cli runtime prepare" to validate the selected engine, then "miner-cli up" to start the deployment. 5. Inspect the Result Check deployment status, logs, and the model endpoint. If the miner agent is enabled, inspect its local status and identity endpoints as well. Why this matters A successful deployment depends on several layers working together. BTTInferGrid's setup process makes those dependencies explicit, giving operators a structured path from host preparation to a running inference service. The requirements are technical: this documented workflow is designed for Linux hosts with NVIDIA GPUs, not for every ordinary computer. Because deploying decentralized AI starts with getting the fundamentals right. @BitTorrent @justinsuntron #BitTorrent #BTTInferGrid #DeAI #GPUComputing #TRONEcoStar