How I built a GPU job matching system for decentralized AI inference

The Challenge When you have hundreds of GPU nodes with different specs (VRAM, TFLOPS, models supported) scattered worldwide, how do you route an inference request to the right node in milliseconds? This is the core engineering problem behind NeuralGrid, the decentralized GPU network I'm building. Here's how I solved it. Architecture Overview Client Request → API Gateway → Job Matcher → Node Selection → Inference → Response ↓ ↓ Auth + Rate Score each node: Limiting - Available VRAM - TFLOPS capacity - Network latency - Current load The Matching Algorithm Each node reports its specs when it joins the network: interface NodeSpec { gpu_model: string; // "RTX 4090", "A100", etc. vram_gb: number; // Available VRAM tflops: number; // Compute capacity status: string; // "online" | "busy" | "offline" } When a job comes in, the matcher scores every online node: function scoreNode(node: NodeSpec, job: InferenceJob): number { if (node.status !== 'online') return -1; if (node.vram_gb < job.minVram)