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Open source and on device: the strategy behind Nvidia’s new AI models
The latest Nemotron 3.5 models show how the AI giant is focusing on reinforcing its own GPU infrastructure while supporting the open source community.
Amid the accelerating race between open and closed AI, the vendor advanced its open source line of attack with more open models.
Nvidia earlier this week introduced Nemotron 3.5 Super VL, a 120-billion-parameter model that it said delivers fast-track completion, or the ability for an AI model to complete a task nearly instantly, and accuracy with "always on" agents.
Along with that top-of-the-line model, Nvidia released Nemotron 3.5 Bolt 74B-A9B 24GB and Nemotron 3.5 Bolt E4B 4GB. The 64B version is optimized for balance and speed, while the E4B is designed for conversational agents.
All three models are fully open, with Nvidia releasing their weights, datasets and training recipes. The releases come after Nvidia released another fully open model, Nemotron 3.5 Lightning, in August.
The models are available on Hugging Face. They are the first models Nvidia has released since it agreed to acquire the open source AI platform last month. The release highlights how the open model paradigm has evolved over the past few years since Meta first released its Llama open-weight model family.
In recent years, open models have grown markedly larger, highlighted by Meta's 405-billion-parameter Llama model, introduced in 2024.
"What we think of as small models has gotten quite bigger,” said Alexander Harrowell, an analyst at Omdia, a division of Informa TechTarget.
He added that this change should benefit enterprises, because they can better fine-tune and customize models. Moreover, enterprises now enjoy access to more models and more open models are on the market, allowing for better competition.
Nvidia and open source
With the release of the latest Nemotron models, Nvidia is sustaining its backing of open source initiatives and continues to lead the campaign in the U.S. AI market to develop open models to better compete with low-cost, high-performance Chinese open-weight models.
A recent example came earlier this week, when Nvidia made its GPU orchestration and AI workload platform Run.ai open source.
“They are concerned about the need for an open AI ecosystem,” Harrowell said, adding that this motivation is not entirely pure, because Nvidia also has self-interest in mind.
While Nvidia is releasing open models to support the open source community, that clearly is not the only reason, according to Harrowell.
“They’re doing this to support the wider ecosystem around their GPUs,” he said.
At the same time, some of Nvidia’s customers boast their own AI chips. For instance, Google offers its TPUs, Amazon offers Trainium AI accelerator chips, and OpenAI is developing its own AI chip, Jalapeño.
“You could see this as an [industry-wide] trend toward integration,” Harrowell said, referring to the idea that as Google, Amazon and OpenAI develop their own chips, they can integrate those chips and optimize them with their AI models. “Close integration of models, model software and custom chips."
He noted that most open-weight models are quickly replaced as citizen developers switch from older open models to newer ones.
“I can see people literally right now talking about how they're replacing Qwen 3.6 35B with Nemotron Bolt straight away,” Harrowell said, referring to Chinese tech giant Alibaba’s popular open model.
Agentic AI and local computers
Nvidia’s focus on specialized agents with the release of the latest Nemotron models is important for the open source community, Harrowell continued.
"A lot of developers are looking for a good open source base model for code to support customized agents," he said. He added that the architecture of the Nemotron 3.5 Super VL and Bolt models enables better memory scaling, leading to a bigger context window, which is valuable in agentic AI.
Meanwhile, one of the big questions in the industry is whether AI can be moved from data centers onto local PCs or shared devices such as Nvidia DGX Spark and Apple’s Mac Minis.
One bottleneck is enterprises getting used to the idea of packaged software running locally on the computer again, rather than only in the cloud or as a web service, Harrowell said
Esther Shittu is an Informa TechTarget news writer and podcast host covering AI software and systems.
