AWS shows how to deploy llama.cpp and SemIf on Amazon SageMaker AI
AWS published a guide to text generation and scoring predefined options on Amazon SageMaker AI. It uses llama.cpp and the SemIf adapter; the example includes deployment on both CPU and GPU, along with accuracy and latency measurements.
AWS published a guide covering two paths for deploying models on Amazon SageMaker AI. The standard path uses AWS Deep Learning Containers for llama.cpp and an interface compatible with OpenAI Chat Completions. The second path adds the SemIf adapter, which returns probabilities and logits for predefined options instead of generating a response.
A model in GGUF format is packaged in model.tar.gz and stored in Amazon S3. The published container can be used directly for text generation. The decision-making path requires a derived container because the standard server does not expose the logits and stateful operations needed by SemIf. This container is built using AWS CodeBuild and stored in Amazon Elastic Container Registry; it retains the libraries for CPU or CUDA and replaces the default server with the adapter.
AWS tested the SemIf extension on 231 JevBench tasks using Qwen3.5-4B with Q4_K_M quantization. On an ml.c6i.8xlarge instance with 32 CPU threads, accuracy reached 179/231, or 77.5 %, with a median latency of 1.715 seconds and a 95th percentile of 19.291 seconds. On an ml.g6e.xlarge instance with all computation offloaded to the NVIDIA L40S GPU, accuracy was 180/231, or 77.9 %, with a median of 0.352 seconds and a 95th percentile of 0.599 seconds. Both configurations returned a valid response for every task.
The measurements include network latency and, according to AWS, do not constitute a service guarantee. The configurations used different builds of llama.cpp; the difference of one correct response therefore does not demonstrate higher quality when using a GPU. The source recommends validating the model on labeled examples from your own application, checking what the returned probabilities mean, and keeping permissions and fixed business rules outside the model.
Why it matters
The described architecture makes it possible to route cases to queues, prioritize incidents, or select agent actions using scores for known options, without parsing generated text. The application can then apply its own thresholds and fallback procedures. However, the model still processes the entire input, and its probabilities require validation for the specific use case.
Two audiences, two different impacts
What this means
For individuals
Developers have a concrete procedure for choosing an interface based on the task: the published container is sufficient for text generation, while access to logits and scoring fixed options requires a derived container with the SemIf adapter.
For a business
For a company automating case routing or prioritization, the guide shows how to separate model evaluation from mandatory process rules. Measurements by AWS also demonstrate substantially different latency for specific CPU and GPU configurations, which needs to be considered when designing service response times.
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