$ ./ai-factory/nim
NIM Inference Microservices_
A model is only useful when someone can call it. NVIDIA NIM turns models into production endpoints in minutes, running on your own hardware, so prompts, outputs, and weights never leave the building.
What's in scope
$ nim deploy --model llama-3.1 --profile autoNIM Deployment & Sizing
Endpoint architecture matched to your models, traffic, and latency targets.
$ nim optimize --engine tensorrt-llmInference Optimization
TensorRT-accelerated serving tuned for throughput, cost, and response time.
$ kubectl autoscale nim --min 2 --max 40Autoscaling on Kubernetes
Endpoints that scale with demand across on-prem GPUs and cloud.
$ nim serve --network private --tls onPrivate Endpoints & Security
Self-hosted inference that keeps prompts, outputs, and weights inside your walls.
$ nim rollout v2 --strategy a-bModel Catalog & Lifecycle
Versioned rollout, A/B serving, and clean retirement of models in production.
$ nim metrics --watch latency,tokensServing Observability
Latency, token, and quality metrics wired into your monitoring stack.
The rest of the factory
Run:ai GPU Orchestration
Fractional GPUs, quotas, and fair-share scheduling, so every team gets compute without contention.
Learn moreAgentic AI
AI agents built on NIM and NeMo that reason, use tools, and automate real enterprise workflows.
Learn moreIsaac Sim & Robotics
Robot simulation, synthetic data generation, and sim-to-real workflows built on NVIDIA Isaac Sim.
Learn moreOmniverse & Digital Twins
Photorealistic digital twins crafted by in-house digital artists, backed by GPU simulation.
Learn moreDGX & SuperPOD Infrastructure
The factory floor itself: NVIDIA workstations, servers, and SuperPODs sized, cabled, and run as one system.
Learn more