Nvidia launches NIM Agent Blueprints to speed AI use
Nvidia introduced new microservice that helps enterprise developers deploy GenAI applications. It also introduced three Blueprints for different use cases.
AI hardware and software vendor Nvidia Corp. launched a new NIM microservices on Tuesday that aim to speed up AI app development.
NIM Agent Blueprints is a catalog of pre-trained, customizable AI workflows that enterprise developers can use to build and deploy generative AI applications.
The first three NIM Agent Blueprints introduced are digital humans, or avatars for customer service; multimodal PDF Data Extraction for Enterprise Retrieval Augmented Generation; and Generative Virtual Screening for Drug Discovery.
NIM Agent Blueprints give developers a head start when creating applications with AI agents, Nvidia said.
Blueprints include sample applications built with Nvidia NeMo, Nvidia NIM, and partner microservices.
NIM Agent Blueprints follow a GenAI market trend where many enterprises seek to move from ideation to implementation.
Thus, Nvidia NIM attempts to solve some of the problems enterprises face implementing GenAI technology, including the speed at which enterprises integrate GenAI workflows, said Chirag Dekate, an analyst with Gartner.
"It's about creating pluggable, modular components that enterprises can build off," Dekate added.
While Nvidia could have chosen to give developers AI tools to build their applications, with NIMs such as NIM Agent Blueprints, the vendor provides enterprises with pretrained models that allow them to move quicker in building generative AI products and services, Futurum Group analyst Olivier Blanchard said.
While this is cost-effective, it might not be what every enterprise needs, Blanchard said.
"For enterprises that are super unique, NIM might not be the model," he said. "They might start somewhere else first and then use them to build additional layers of capabilities or features on top of their other product."
What Nvidia is providing is not unique to GenAI technology, said Rob Enderle, an analyst with Enderle Group.
"Part of what's required to be successful in the market, you have to have a complete solution, not just the technology," Enderle said. "Initially, generative AI was a technology. What Nvidia is doing is turning it into a complete solution."
Nvidia is not the only vendor endeavoring to make generative AI more than just a technology.
AMD, a competitor to Nvidia, is one of the leading companies in the movement, Enderle said.
The vendor last week purchased ZT Systems along with several other companies, including Mipsology, an AI software startup in France, to help with AI inference capabilities.
Google, AWS and Microsoft also are building tools to help enterprises easily deploy GenAI systems.
Nvidia software services
NIM Agent Blueprints allows Nvidia to beef up its software services, making services such as NIMs available regardless of the cloud provider.
It's about creating pluggable, modular components that enterprises can build off.
Chirag DekateAnalyst, Gartner
"This is part of the strategic plan that Nvidia is executing to essentially create a vertically integrated alternative no matter where customers are building their solutions at scale," Dekate said. "By enabling these building blocks, like NIM Blueprints, what Nvidia is essentially embedding is higher-level modular architectures that basically pull through the Nvidia stack no matter where you run them."
On Monday, Nvidia also introduced new CUDA libraries, including new LLM applications.
New LLM applications in CUDA include NeMo Curator and SDG, or synthetic data generation.
NeMo Curator is an application that helps developers create custom datasets in LLM use cases.
Synthetic data generation is an LLM application that augments existing datasets with synthetic data to fine-tune models and LLM applications.
Esther Ajao is a TechTarget Editorial news writer and podcast host covering artificial intelligence software and systems.
