Deploy decision models with llama.cpp on Amazon SageMaker AI
Amazon Web Services Jyothirmai Kottu
The post shows how to deploy decision models using llama.cpp on Amazon SageMaker AI, including an OpenAI-compatible text-generation endpoint and an optional SemIf extension for structured option probabilities. It benchmarks SemIf on 231 JevBench tasks and reports the GPU endpoint is 32.2 times faster at p95 than the CPU setup. As a result, you can route requests over a fixed set of options using probability outputs without generating an answer token, and choose CPU for seconds-level latency or GPU for tighter tail latency.
Why it matters
Learn how to deploy decision models with llama.cpp on Amazon SageMaker AI. 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 or GPU.
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