Deploy decision models with llama.cpp on Amazon SageMaker AI
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This AWS blog post details how to deploy decision models using llama.cpp on Amazon SageMaker AI, offering a managed path for serving quantized models on CPU or GPU infrastructure. It outlines two deployment paths: a standard text-generation route using OpenAI-compatible chat completions and a structured-decision route utilizing the SemIf adapter to return probabilities over fixed options. The article explains that while the standard DLC serves generated responses, the SemIf extension builds a child image to access final-position logits without decoding answer tokens. Benchmarks comparing Qwen3.5-4B models show that GPU endpoints are significantly faster than CPU ones, though accuracy remains similar across both configurations. The authors provide step-by-step instructions for packaging GGUF models, deploying endpoints via CodeBuild, and invoking them with specific JSON schemas for decisions. They recommend using CPU for low-throughput workloads and GPU for interactive applications requiring tight tail-latency targets. Finally, the post emphasizes maintaining application-level thresholds and fallbacks rather than relying solely on model outputs for operational confidence.
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