Deploy decision models with llama.cpp on Amazon SageMaker AI

What happened

This technical how-to serves OpenAI-compatible chat completions with the AWS Deep Learning Container for llama.cpp, then extends the runtime with SemIf to return probabilities over declared options on CPU (the general-purpose processor at the centre of a computer) or GPU (a chip built for many calculations at once, used for graphics and AI). Applications that route requests, select agent actions, or assign policy outcomes often need scores over a fixed set of choices instead of free-form text.

Deploying these decision models with llama.cpp on Amazon SageMaker (Python library) AI gives you a managed path for serving quantized models on CPU or GPU infrastructure. Previous posts cover WhisperX , vLLM-Omni – Part 1 , and vLLM-Omni – Part 2 .

You then extend the same runtime with SemIf , formerly OpenJev, which returns probabilities over declared options without decoding an answer token. The accompanying code sample contains CPU and GPU deployment options.

Key facts

• The accompanying code sample — includes: CPU and GPU deployment options