A visual comparison of three ML model deployment strategies — serverless, on-premise, and edge — covering their tradeoffs in latency, cost, and privacy. Serverless suffers cold starts up to 90 seconds; on-prem avoids cold starts but requires careful GPU sharing to be cost-effective; edge runs on-device but requires model compression. The post highlights a key on-prem pitfall: tools like vLLM and HuggingFace TEI each monopolize a GPU, forcing teams to run multiple cards for small models. The Superlinked Inference Engine (SIE) is presented as an open-source solution that multiplexes embeddings, reranking, and generation on a single GPU with LRU memory management. The newsletter also covers five AI hardware architectures (CPU, GPU, TPU, NPU, LPU) and a signal-based approach to sampling agent trajectories for review without using an LLM evaluator.
