Decentralized AI Inference Needs a Boring Job Queue

This story was originally published on HackerNoon at: https://hackernoon.com/decentralized-ai-inference-needs-a-boring-job-queue. A backend engineer's proposal for reliable decentralized GPU inference using job queues, retries, and idempotency, so remote AI jobs don't fail or bill twice. Check more stories related to tech-stories at: https://hackernoon.com/c/tech-stories. You can also check exclusive content about #decentralize-ai-hackathon, #decentralized-ai, #software-engineering, #fault-tolerant-systems, #distributed-computing, #bullmq, #inference-api, #duplicate-billing, and more. This story was written by: @mirabilis. Learn more about this writer by checking @mirabilis's about page, and for more stories, please visit hackernoon.com. Decentralized AI needs reliable backend plumbing, not just GPUs. This proposal treats remote GPU inference like a normal backend service: an API returns a job ID, BullMQ, Redis and PostgreSQL track the work, and a provider adapter (starting with Nosana) runs it. Idempotency keys, deadlines, and recovery checks prevent duplicate runs and double billing. It's a proposal, not a benchmarked product.