First, feel free to watch the overview video:
👉 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.
