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Home»ADOPTION NEWS»NVIDIA NIM Simplifies Generative AI Deployment for Developers
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NVIDIA NIM Simplifies Generative AI Deployment for Developers

By Crypto FlexsJune 2, 20243 Mins Read
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NVIDIA NIM Simplifies Generative AI Deployment for Developers
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Accelerate Deployment of Generative AI with NVIDIA NIM

NVIDIA has launched a new tool to simplify deployment of generative AI models for enterprise developers. According to the NVIDIA Technology Blog, this solution, known as NVIDIA Inference Microservices (NVIDIA NIM), provides an optimized and secure path to deploy AI models both on-premises and in the cloud.

NVIDIA NIM is part of the NVIDIA AI Enterprise family, providing a powerful platform for developers to iterate quickly and build advanced generative AI solutions. The tool supports a wide range of pre-built containers that can be deployed with a single command on NVIDIA accelerated infrastructure, ensuring the ease of use and security of enterprise data.

Key features and benefits

One of the great features of NVIDIA NIM is the ability to deploy NIM instances in less than 5 minutes on NVIDIA GPU systems, including in the cloud, data centers, local workstations, and PCs. Developers can also prototype applications using NIM APIs from the NVIDIA API Catalog without having to deploy containers.

  • Pre-built containers that can be deployed with a single command.
  • Secure and controlled data management.
  • Supports fine-tuned models using technologies such as LoRA.
  • Integrates with industry standard APIs for accelerated AI inference endpoints.
  • Compatible with popular generative AI frameworks such as LangChain, LlamaIndex, and Haystack.

This comprehensive support allows developers to integrate accelerated AI inference endpoints using consistent APIs and effectively leverage the most popular generative AI application frameworks.

Phased Deployment

The NVIDIA Technology Blog provides a detailed walkthrough on how to deploy NVIDIA NIM using Docker. The process begins with establishing the necessary prerequisites and obtaining an NVIDIA AI Enterprise License. Once set up, developers can run a simple script to deploy the container and test inference requests using curl commands. This setup ensures a controlled, optimized production environment for building generative AI applications.

Integration with popular frameworks

For those looking to integrate NIM with existing applications, NVIDIA provides sample deployments and API endpoints through the NVIDIA API Catalog. This allows developers to use NIM in Python code through the OpenAI library and other frameworks such as Haystack, LangChain, and LlamaIndex. This integration provides secure, reliable, and accelerated model inference to developers who are already using popular tools.

Maximize NIM capabilities

NVIDIA NIM allows developers to focus on building high-performing, innovative generative AI workflows. The tool supports additional enhancements, such as using microservices with customized LLMs as LoRA adapters, ensuring developers can achieve the highest accuracy and performance for their applications.

NVIDIA regularly releases and enhances NIM, which provides a variety of microservices for vision, search, 3D, digital biology, and more. We recommend that developers visit the API Catalog frequently to stay up to date on the latest products.

Image source: Shutterstock

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