NVIDIA is moving beyond hardware to software ecosystem dominance
It seems like few are noticing it, but NVIDIA is building a comprehensive software ecosystem…
There is something I have been noticing, that is NVIDIA’s groundswell around software and models….in this post I will be trying to give language to it…
NVIDIA’s release of the Nemotron-Nano-12B-v2-VL-FP8 and related models fit neatly into their broader push on open-sourcing efficient and versatile models.
This includes specialised small language models which can be orchestrated in Agentic Workflows, all the while tightening the loop around their hardware stack.
NVIDIA’s explicitly framing SLMs as the backbone for scalable agentic systems, where you compose multiple lightweight specialist models (for example, one for vision-RAG, another for guardrails) rather than relying on giant monolithic model.
Their recent research paper and dev blogs emphasises this, that SLMs are economical and technically suitable for agentic workflows because they match or beat larger models on tool-use/coding tasks, run edge-side without cloud dependency and enable faster iteration across orgs.
The Nano VL variant is tuned for exactly that — e.g., extracting invoice data from videos/images or summarising multi-doc comparisons, making it plug-and-play for agent orchestration.
Looking at the example below, the invoices are from a dataset in HuggingFace, and leveraging the SLM, the invoices can be uploaded, and questions can be asked like
Sum up all the totals across the receipts
or
Here are 4 invoices flagged as potential duplicates — are they actually the same document with minor layout differences?
This is an interesting example because the model is able to perform spatial reasoning by comparing multiple invoices (images) in real-time.
Back to the hardware…
Open models like Nemotron lower the barrier for developers to experiment, but they’re optimised for NVIDIA hardware.
Their new DGX Spark workstation is a compact ARM64-based personal AI supercomputer for prototyping agents and models right on your desk.
It’s pitched as the entry-point for researchers to build in NVIDIA’s environments, ensuring local work ports to enterprise without friction.
No easy AMD/Intel swaps, but that’s the point right?
NVIDIA is creating momentum for their hardware moat.
And I think the challenge is that NVIDIA is really the most advanced with their approach to model orchestration, continuous fine-tuning and the data flywheel for a real-time feedback loop.
For me, the biggest impediment to experimenting with NVIDIA was access and cost of hardware, this is changing with the Spark.
With NVIDIA is moving into consumer hardware.
Below is the full post to the NVIDIA data flywheel approach…
NVIDIA Tool-Calling Data Flywheel for Smarter, Smaller Language Models— A Practical Guide
This practical example shows the Fine-Tuning of LLMs to optimise them for Tool Calling.cobusgreyling.medium.com
So once you have the hardware and the environment is ready, you have access to so many models, notebooks and cookbooks…this is NVIDIA’s opportunity to capture the way of work and how best practices are seen.
The example below shows how a presentation in PDF is uploaded, and highly contextual questions can be asked based on the presentation.
text_prompt = “How much did Data Center business grow in Q2 FY26?”
text_prompt = “Which business unit had the most growth Y/Y?”
In Short
Small Language Models (SLMs) will be orchestrated to perform specific tasks in agentic workflows.
SLMs will be fine-tuned on a regular cadence based on a data flywheel.
Usage data will be curated and used to optimise different aspects of the agentic workflow.
SLMs will be optimised to perform specific pinpointed tasks.
Laser focus will be placed on two aspects, accuracy of tool selection and orchestration of multiple/parallel to optimise for inference latency.
What is holding us back, is compute.
I see NVIDIA DGX Spark is the first step in enabling all of this. Giving access to individuals to freely prototype, fine-tune, perform production grace inference and build edge applications.
Chief Evangelist @ Kore.ai | I’m passionate about exploring the intersection of AI and language. Language Models, AI Agents, Agentic Apps, Dev Frameworks & Data-Driven Tools shaping tomorrow.
nvidia/NVIDIA-Nemotron-Nano-12B-v2-VL-FP8 · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.huggingface.co
Regarding NVIDIAs software push, it makes me wonder what completely new possibilities these SLMs will unlok for edge computing. Your insights into agentic workflows are always so sharp!
This is a really insightful take on NVIDIA's strategy. I think what's particlarly clever is how they're using SLMs and open-source models to create sticky developer ecosystems without sacrificing their hardware moat. The DGX Spark as an entry point is smart too - it lowers the barrier for experimentaton while ensuring that production workloads will naturally scale to their enterprise offerings. The data flywheel concept you mentioned is probaly the most underappreciated part of this - continuous fine-tuning from usage data creates compound advantages that are hard to replicate. It's the classic playbook: democratize at the edges, capture at the core.