Microservices

NIM (NVIDIA Inference Microservices) are container images and Helm charts that deliver ready-to-run inference endpoints for specific models (LLMs, vision, embedding, reranking, and more). The objective is to shrink time-to-production: instead of assembling CUDA drivers, frameworks, model weights, and an OpenAI-compatible server yourself, operators pull a NIM that bundles a performance-tuned engine (often TensorRT-LLM or Triton-backed paths), default model artifacts or download hooks, health checks, and a stable HTTP/gRPC API. NIMs are sized for GPU deployment and target enterprise MLOps teams that want versioned, scannable containers with predictable resource requests rather than bespoke notebooks turned into scripts.