NVIDIA Inference Microservice (NIM)

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First, feel free to watch the overview video:

Digital Humans: NVIDIA Announces NIMs 🀯 Containerized AI Microservices

πŸ‘‰ Source: https://www.nvidia.com/en-us/ai/

Overview

  • NVIDIA NIM Launch: Available for 28 million developers to easily build generative AI applications for various use cases like copilots and chatbots, reducing deployment time from weeks to minutes.
  • Productivity Boost: Simplifies adding generative AI to applications using standardized, optimized containers for multiple model types (text, images, video, speech).
  • Infrastructure Efficiency: Running Meta Llama 3-8B with NIM produces up to 3x more generative AI tokens, enhancing efficiency and boosting infrastructure utilization.
  • Widespread Integration: Nearly 200 partners, including Cadence, Cloudera, Cohesity, DataStax, NetApp, Scale AI, Synopsys, and Hugging Face, are integrating NIM to accelerate generative AI deployments.
  • 40+ NIM Microservices: Supports a wide range of generative AI models, including Databricks DBRX, Meta Llama 3, Microsoft Phi-3, and more, available as endpoints on ai.nvidia.com.
  • Healthcare and Digital Biology: NIM supports applications in healthcare and digital biology, powering tasks like surgical planning, digital assistants, drug discovery, and clinical trial optimization.
  • Interactive Digital Humans: New NVIDIA ACE NIM microservices enable building lifelike digital humans for customer service, telehealth, education, gaming, and entertainment.

NIM Industry Use Cases

  • Foxconn: Uses NIM for domain-specific LLMs in AI factories.
  • Pegatron: Advances local LLM development with Project TaME.
  • Amdocs: Enhances customer billing accuracy and response time.
  • Lowe’s: Improves customer and associate experiences.
  • ServiceNow: Integrates NIM for scalable LLM development.
  • Siemens: Uses NIM for shop floor AI and Industrial Copilot.