NVIDIA NIM AI features and pricing (October 2026)

NVIDIA NIM is a set of prebuilt inference microservices that package AI models, optimized engines and standard APIs in containers. Platform teams use it to self-host models on NVIDIA GPUs in the cloud, in their data center or on workstations.

Each NIM container bundles a model, an inference engine, industry-standard APIs and runtime dependencies. For language models, NIM supports models served by TensorRT-LLM, vLLM or SGLang, including community models and models fine-tuned on your own data, and it picks a runtime engine that fits the available GPUs. A single docker command starts the service, which then answers OpenAI-style requests on a local endpoint.

The catalog at build.nvidia.com offers free hosted endpoints for prototyping, and Helm charts support scaling on Kubernetes. NVIDIA Developer Program members may self-host NIM for research, development and testing on up to 16 GPUs; production use comes with NVIDIA AI Enterprise. NIM is a product of NVIDIA Corporation, based in Santa Clara, California.

• Deploys a model with one commandA NIM container pulled from NVIDIA's registry starts with a single docker run command and exposes the model on a local HTTP endpoint. Platform teams avoid assembling engines, drivers and dependencies by hand for each model they need to serve.
• Picks the inference engine per GPUNIM runs the model on an optimized engine such as TensorRT, TensorRT-LLM, vLLM or SGLang, chosen for the NVIDIA hardware it finds. In NVIDIA's own benchmark with Llama 3.1 8B on one H100, throughput rose from 613 to 1,201 tokens per second with NIM.
• Keeps application code portableSelf-hosted NIM endpoints accept requests from the OpenAI client library once the base URL points to them. According to NVIDIA, teams can prototype against hosted endpoints at build.nvidia.com and move to their own infrastructure without code changes.
• Scales inference on KubernetesNVIDIA provides Helm charts and guides for running NIM on Kubernetes, plus detailed observability metrics for dashboards. Operations teams can run NIM like other containerized services and watch load and latency of each model endpoint.

• Built for NVIDIA-accelerated infrastructure, from RTX AI PCs and workstations to data centers and public clouds.
• Free self-hosting for research, development and testing on up to 16 GPUs through the NVIDIA Developer Program.
• Production use is licensed through NVIDIA AI Enterprise, which includes enterprise support and starts with a free 90-day trial license.
• Hosted endpoints at build.nvidia.com let teams prototype before they run containers on their own hardware.

NVIDIA Corporation is headquartered in Santa Clara, California. NIM containers run on your own GPU infrastructure, so use does not depend on the country.

Plan the license path early: the Developer Program covers research, development and testing, while production use requires NVIDIA AI Enterprise, priced through NVIDIA or its partners. Check that your GPUs support the models you need and that your clusters can pull images from NVIDIA's registry. Self-hosted NIM keeps data inside your environment; for the hosted endpoints, review NVIDIA's terms before sending real data.

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