Related Software

NVIDIA NIM for Large Language Models (LLM) and Vision Language Models (VLM) fits into a broader inference and platform ecosystem. The following software products are highly relevant when you are deploying, operating, or extending LLM workloads. For capabilities of NIM LLM and VLM itself, refer to Overview.

NVIDIA NIM Within the Inference Ecosystem

The following table lists related products and how each one relates to NIM LLM and VLM:

Product	Relation to NIM LLM and VLM
vLLM	NVIDIA NIM for Large Language Models (LLM) and Vision Language Models (VLM) packages vLLM as its inference backend, so many request semantics and tuning concepts come directly from vLLM.
NIM Operator	The operator manages NIM deployments by using Kubernetes custom resources and is especially useful for repeatable, production-scale rollouts.
NVIDIA NIM for Vision Language Models	Earlier VLM-specific documentation remains useful for deployments that still follow that path. Use these NIM LLM and VLM docs for the combined LLM and VLM workflow.

Choosing Related Software

To help you choose the right tool for your specific use case, consider the following recommendations:

• Use NIM LLM and VLM when your workload uses text, image, audio, or video input and you want a curated, enterprise-ready container for production inference.

• Use vLLM documentation alongside NIM documentation when you need deeper, backend-specific context for passthrough arguments or upstream model-serving behavior.

• Use the NIM Operator when your primary deployment target is Kubernetes and you want lifecycle automation around NIM services.