AWS details decision model deployment with llama.cpp and SemIf on SageMaker AI

AWS published a guide to deploying GGUF models with its llama.cpp Deep Learning Container on Amazon SageMaker AI and adding SemIf, formerly OpenJev, to return probabilities over declared options without generating answer tokens. The guide provides CPU and GPU deployment code, starting with OpenAI-compatible chat completions served through SageMaker AI InvokeEndpoint before extending the runtime for decision tasks. The text-generation example uses a fixed CPU instance. The SemIf extension also defaults to CPU for workloads that are not latency-sensitive, with an optional CUDA image and ordered instance pools for GPU deployment. AWS reported that, across 231 JevBench tasks using Qwen3.5-4B Q4_K_M, the tested GPU configuration delivered 4.9 times faster p50 latency and 32.2 times faster p95 latency than the tested CPU configuration, with similar accuracy.

Generated by AI from reports · Updated 5 days ago