AWS Enables Decision Model Deployment with llama.cpp on SageMaker
AWS released a guide for serving quantized llama.cpp models on SageMaker, offering both chat‑completion and structured‑decision endpoints with CPU or GPU options.
Original source published: October 12, 2026
The AWS blog post details two deployment paths for llama.cpp models on SageMaker AI. The standard path uses the AWS Deep Learning Container for llama.cpp to expose an OpenAI‑compatible chat completions API, while the structured‑decision path builds a child image that replaces the default server with a SemIf adapter, returning option probabilities instead of generated text. Both paths accept a GGUF model stored in Amazon S3 and map POST /invocations to the appropriate endpoint.
For the structured‑decision workflow, the child image is built with CodeBuild, retains the DLC’s compiled CPU or CUDA libraries, and is stored in Amazon ECR. Benchmarks show the GPU endpoint processing decisions 4.9× faster at the median (p50) and 32.2× faster at the 95th percentile compared with the CPU endpoint, with comparable accuracy. The guide also lists prerequisites such as an AWS account, S3 bucket, IAM roles, and compatible SageMaker instance types.
These instructions give developers a concrete example of integrating decision‑scoring models into production, illustrating how to balance latency, hardware choice, and model quantization when building AI‑driven routing or policy systems.