NVIDIA NIM: Containerized AI Inference Microservices

SUMMARY

In 30 seconds

In a July 2024 short produced in partnership with NVIDIA, Matthew Berman introduces NIM as a way to deploy optimized AI inference services. He mentions local and cloud deployment and several examples; hardware support and availability depend on the specific service and configuration.

1Packaged inferenceBerman describes containerized AI services intended to simplify application deployment.
2Local and cloud optionsHe discusses local systems and NVIDIA cloud inference; actual requirements depend on the model and service.
3Examples and sponsorshipThe video mentions Llama 3 8B, NVIDIA ACE and retrieval-augmented applications, and discloses a partnership with NVIDIA.

Takeaways

NIM packages AI inference as containerized services with APIs and runtime optimizations.
Deployment can be local or cloud-based depending on the NIM and compatible hardware.
Local deployment can help keep data on premises, but it does not automatically guarantee security.

TopicsInfrastructure & deployment Models & training

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NIM deployment overview

Berman introduces the service, deployment options and examples in a sponsored short.

NVIDIA NIM packages model inference as containerized microservices with runtime optimizations and APIs, helping developers incorporate AI capabilities into applications. I describe deployment on compatible local workstations or desktops and through cloud inference services. A NIM is an inference service, not necessarily a complete application, and requirements vary by model.

At the time of this video, I mention Llama 3 8B, NVIDIA ACE, retrieval-augmented generation and an NVIDIA model as examples of the ecosystem. Local deployment can help keep data on premises, but security still depends on configuration and access controls. This video was made in partnership with NVIDIA.