Deploy decision models with llama.cpp on Amazon SageMaker AI

AWS Machine Learning has released a new deployment strategy that uses the llama.cpp runtime within an AWS Deep Learning Container (DLC) to serve decision models on Amazon SageMaker AI. The approach allows users to host quantized models on either CPU or GPU infrastructure, providing a managed path for serving OpenAI‑compatible chat completions through SageMaker AI InvokeEndpoint. In addition to the standard text‑generation capability, the post introduces SemIf, formerly known as OpenJev, which extends the same runtime to return probabilities over a fixed set of declared options without decoding an answer token. This feature is useful for applications that need to route requests, select agent actions, or assign policy outcomes based on discrete choices rather than free‑form text. The accompanying code sample demonstrates how to deploy the model on both CPU and GPU, illustrating the flexibility of the solution. The post is part of a series on specialized AWS DLCs for generative AI inference, following earlier coverage of WhisperX, vLLM‑Omni Part 1, and vLLM‑Omni Part 2. By providing a streamlined, containerized workflow for decision‑making models, AWS enables developers to integrate more sophisticated AI decision logic into their applications with reduced operational overhead.