Deploy decision models with llama.cpp on Amazon SageMaker AI | Amazon Web Services

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 AI gives you a managed path for serving quantized models on CPU or GPU infrastructure.

This post continues a series on specialized AWS Deep Learning Containers (DLCs) for generative AI inference. Previous posts cover WhisperX, vLLM-Omni – Part 1, and vLLM-Omni – Part 2.

In this post, you first use the AWS DLC for llama.cpp to serve OpenAI-compatible chat completions through SageMaker AI InvokeEndpoint. 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 includes CPU and GPU deployment options.

Solution overview

Figure 1 shows two deployment paths from the same packaged model. The text-generation path uses the...

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