Running GPU-powered ML inference in production is notoriously hard — you either pay for idle GPU hours, wrestle with CUDA driver hell, or hit cold-start walls that make your model unusable at scale. For African developers and startups, expensive cloud GPU reservations can be a showstopper before you even ship a demo. This talk walks through how serverless GPU inference with Modal solves this problem: you write pure Python, define your container and GPU requirements as code, and only pay for the milliseconds your inference actually runs. This talk will cover my experience building serverless GPU inference engine for low-resource language and low-budget inference engine. During this talk we will build a fully functional image-generation, transcription inference with Whisper and LLM inference service live on stage — deploying a Stable Diffusion pipeline, transcription service with modal and a quantized LLaMA model from scratch, with cold-start optimizations, batching, and autoscaling — all within a free-tier budget. You'll leave with a working template, a mental model for GPU-first Python design, and the confidence to ship ML-powered APIs without owning a single GPU.