ServiceNow and Nvidia just dropped Apriel Nemotron 15B, a compact open-source reasoning model built to run enterprise AI agents at lower latency and inference costs. Delivered as an Nvidia NIM microservice, it pairs with a new joint data flywheel architecture that feeds real enterprise workflow data back into model training.
Nvidia just made its NeMo microservices generally available, offering end-to-end tools to customize, evaluate, and guardrail enterprise AI agents. It boosts fine-tuning throughput by up to 1.8x and helps firms like AT&T and Cisco tap into continuous data flywheels. Basically, it turns enterprise LLMs into constantly learning digital coworkers.
NVIDIA just introduced the Llama Nemotron and Cosmos Nemotron model families, custom-built to power autonomous AI agents across enterprises. Available in Nano, Super, and Ultra sizes, the open models use distillation and pruning to run everywhere from edge PCs to multi-GPU clusters. Why it matters: It gives developers purpose-built open building blocks to deploy multi-agent workflows without bleeding compute budgets.
Nvidia just teamed up with orchestration leaders like LangChain and CrewAI to release new enterprise-ready Agentic AI Blueprints. The catalog includes pre-built templates for turning PDFs into conversational podcasts, automating code docs, and orchestrating multi-agent systems, letting developers spin up reasoning workflows in a single click via NVIDIA Launchables.
NVIDIA just broke down how its AI Foundry service helps companies build custom generative AI models using proprietary data. Powered by DGX Cloud, the NeMo platform, and support for Llama 3.1, it lets businesses forge and deploy tailored models as NIM microservices without building infrastructure from scratch.
NVIDIA just unveiled AI Workbench, a unified developer toolkit that lets teams test and customize pretrained generative AI models locally on RTX PCs and workstations before scaling to the cloud. Alongside the tool, NVIDIA introduced AI Enterprise 4.0 with integrated NeMo support to bridge the gap between desktop experimentation and production-grade deployments.
