Legal and Acknowledgements

This page contains the primary legal references and third-party acknowledgements for NVIDIA NIM for Large Language Models (LLM) and Vision Language Models (VLM).

NVIDIA AI Product Agreement

By using this NIM, you acknowledge that you have read and agreed to the NVIDIA AI Product Agreement.

NOTICE AND DISCLAIMER: This software automatically retrieves, accesses or interacts with external materials. Those retrieved materials are not distributed with this software and are governed solely by separate terms, conditions and licenses. You are solely responsible for finding, reviewing and complying with all applicable terms, conditions, and licenses, and for verifying the security, integrity and suitability of any retrieved materials for your specific use case. This software is provided “AS IS”, without warranty of any kind. The author makes no representations or warranties regarding any retrieved materials, and assumes no liability for any losses, damages, liabilities or legal consequences from your use or inability to use this software or any retrieved materials. Use this software and the retrieved materials at your own risk.

Open Source Software License Acknowledgements

NVIDIA NIM for Large Language Models (LLM) and Vision Language Models (VLM) (NIM LLM and VLM) is built on the work of open source communities whose ongoing innovation drives the LLM inference ecosystem forward. NIM LLM and VLM runs on one of two open source inference engines, vLLM or SGLang, selected by the container image you deploy. NVIDIA is grateful for the collaboration, contributions, and shared commitment to advancing AI infrastructure that these projects represent.

vLLM

vLLM is the inference engine packaged in the vLLM-based NIM LLM and VLM container images. The project provides a high-throughput, memory-efficient serving engine for large language models, and those images package vLLM directly as their inference backend.

NVIDIA recognizes the vLLM community for:

• Foundational inference technology. vLLM introduced PagedAttention, a novel memory management algorithm for KV cache, and has subsequently implemented cutting-edge theory such as continuous batching, automatic prefix caching, and chunked prefill into a production-grade, open source engine. These capabilities form the backbone of every NIM LLM and VLM deployment that runs on the vLLM backend.

• Sustained collaboration. NVIDIA engineers contribute upstream to vLLM, and NVIDIA is dedicated to continuing this investment. Improvements flow in both directions - NVIDIA contributes optimizations to the vLLM project and community-driven innovations are available to NIM LLM and VLM users.

• An open development model. The vLLM project maintains a transparent development process that welcomes contributors across organizations. This openness accelerates the pace of innovation for the entire ecosystem.

For vLLM documentation, refer to the vLLM project documentation.

For details on how NIM LLM and VLM integrates with its inference backend, refer to Architecture.

SGLang

SGLang is the inference engine packaged in SGLang-based NIM LLM and VLM container images. NVIDIA recognizes the SGLang community for its open source serving engine and collaboration on LLM and VLM inference. For backend features and configuration, refer to the SGLang project documentation.

Open Source Projects

NVIDIA NIM for Large Language Models (LLM) and Vision Language Models (VLM) includes open source software components. For the acknowledgements that apply to a specific container, refer to the NVIDIA OSS archive.