Jupyter Notebook
149
798 commits
updated Oct 16, 2026
This repository contains a compilation of examples of optimized deployment of popular Large Language Models (LLMs) utilizing SageMaker Inference. Hosting LLMs comes with a variety of challenges due to the size of the model, inefficient usage of hardware, and scaling LLMs into a production like environment with multiple concurrent users.
SageMaker Inference is a highly performant and versatile hosting option that comes with a variety of options that you can utilize to efficiently host your LLMs. In this repository we showcase how you can take different SageMaker Inference options such as Real-Time Inference (low latency, high throughput use-cases) and Asynchronous Inference (near real-time/batch use-cases) and integrate with Model Servers such as DJL Serving and Text Generation Inference. We showcase how you can tune for performance via optimizing these different Model Serving stacks and also exploring hardware options such as Inferentia2 integration with Amazon SageMaker.
If you are contributing, please add a link to your model below:
Use the SageMaker generative AI deployment Skill to guide an agent through hardware, capacity, cost, deployment, testing, and cleanup decisions. Reflection Beam is included as the first worked example.
See CONTRIBUTING for more information.
This library is licensed under the MIT-0 License. See the LICENSE file.
4,849 followers · starred Jun 2026
22 followers · starred Mar 2025
22 followers · starred Sep 2024
92 followers · starred Oct 2026
Jupyter Notebook
149
798 commits
updated Oct 16, 2026
This repository contains a compilation of examples of optimized deployment of popular Large Language Models (LLMs) utilizing SageMaker Inference. Hosting LLMs comes with a variety of challenges due to the size of the model, inefficient usage of hardware, and scaling LLMs into a production like environment with multiple concurrent users.
SageMaker Inference is a highly performant and versatile hosting option that comes with a variety of options that you can utilize to efficiently host your LLMs. In this repository we showcase how you can take different SageMaker Inference options such as Real-Time Inference (low latency, high throughput use-cases) and Asynchronous Inference (near real-time/batch use-cases) and integrate with Model Servers such as DJL Serving and Text Generation Inference. We showcase how you can tune for performance via optimizing these different Model Serving stacks and also exploring hardware options such as Inferentia2 integration with Amazon SageMaker.
If you are contributing, please add a link to your model below:
Use the SageMaker generative AI deployment Skill to guide an agent through hardware, capacity, cost, deployment, testing, and cleanup decisions. Reflection Beam is included as the first worked example.
See CONTRIBUTING for more information.
This library is licensed under the MIT-0 License. See the LICENSE file.
4,849 followers · starred Jun 2026
22 followers · starred Mar 2025
22 followers · starred Sep 2024
92 followers · starred Oct 2026