NVIDIA takes on agentic AI with Nemotron models
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 is no longer content with just selling the shovels. Now, it wants to polish the dirt off the handles, too.
At CES, the chip giant officially introduced its new Nemotron model families, specifically tuned for enterprise agentic AI. The lineup is split into two branches: Llama Nemotron for reasoning and language tasks, and Cosmos Nemotron for vision-language workloads.
Rather than training massive frontier models from absolute zero, NVIDIA took Meta’s wildly popular open-source Llama foundation and ran it through its own NeMo pipeline. Using pruning, distillation, and advanced alignment, the company optimized these weights specifically for instruction following, function calling, math, and code.
Sized for everything from PCs to data centers
To make deployment practical, both families arrive in three distinct sizes: Nano, Super, and Ultra.
Nano is built for real-time edge devices and local PCs where latency is everything. Super is tuned for high throughput on a single GPU, while Ultra is designed for full-scale data center deployments requiring maximum accuracy.
On the visual side, Cosmos Nemotron aims to give autonomous systems, warehouses, and enterprise video tools the ability to analyze dynamic camera feeds. Enterprise software heavyweights SAP and ServiceNow have already signed on, planning to plug the models directly into platforms like Joule to automate cross-department workflows.
The models will be available for download on Hugging Face and NVIDIA’s catalog, with free API access for NVIDIA Developer Program members.
Why it matters
We think this is NVIDIA playing textbook ecosystem defense.
For enterprise builders, tuning a raw open-weights model to reliably call APIs without hallucinating is painful and expensive. Getting pre-pruned, agent-ready checkpoints that run efficiently on single GPUs saves real engineering hours and infrastructure dollars right now.
Strategically, NVIDIA is leveraging Meta’s open weights to lock developers even deeper into the CUDA stack. By wrapping these models in proprietary NeMo microservices and enterprise software licenses, it ensures that when agents take over enterprise software, they do so on green silicon.
The downside? The lines between hardware vendor and software provider are completely blurring. For teams hoping to stay multi-cloud and chip-agnostic, adopting NVIDIA-distilled agent stacks might make switching to alternative silicon harder than ever.
Sources (1)
- blogs.nvidia.com(opens in a new tab)Originalblogs.nvidia.com