Deployment & Scaling · Practitioner
NVIDIA NIM: Models Packaged as Deployable Microservices
NIM is a container that bundles an optimised inference engine, a server, and an OpenAI-compatible API for a specific model. It turns 'deploy an LLM' from a two-week engineering project into a docker run with a model cache.
- 1.4 hours lesson
- intermediate level
- NVIDIA AI Track
- Narrated presentation
What you’ll learn
- NIM = weights + optimised engine (TensorRT-LLM, vLLM fallback) + server + OpenAI-compatible API in one container
- Automatic engine profile selection based on the detected GPU
- Identical client code against hosted endpoints and self-hosted containers
- LLM, embedding, reranking, speech, vision and biology NIMs; blueprints compose them
- NVIDIA AI Enterprise licensing for production self-hosting, free developer/evaluation tiers
- NIM removes engine toil but not sizing, batching or observability decisions
Inside this lesson
A narrated, slide-by-slide presentation with 1 prediction exercise, 1 knowledge check, 3 videos. You answer as you go, and the platform checks your work.