The days of Nvidia’s unparalleled market dominance are not over, but challenges and options are emerging from all directions.

ZML, a popular French AI startup backed by Turing Award winner Yann LeCun, has released inference performance software that enables running a variety of large open source language models on a variety of chips, including Nvidia, AMD, Google’s TPU, Apple Metal, and Intel Arc.

With ZML/LLMD, the recently launched LLM inference server, the company’s ambition is to break down existing silos and make different chips available for AI use cases at their maximum available speed and sometimes faster, ZML founder Steeve Morin told TechCrunch.

As AI becomes integrated into our work and daily lives, inference optimization (also known as prompt processing) has surpassed model training in importance, but it often feels patchy behind the scenes, with software and architectural barriers that lead to vendor lock-in, Morin said.

The promise of achieving maximum performance in a variety of chips is a technological feat, but it could also be a market disruptor, amid growing fears about AI-related costs.

ZML hopes to offer enterprises and clouds the option of using a combination of chips, some of which could be less expensive or consume less power. “The idea is to give people the power to create their own system and achieve real efficiency gains that allow [AI] to be disseminated,” Morin said.

Such software support could help new AI chip makers, many of which are from Europe, Morin noted, citing Axelera, Fractile, Kalray, OLIX, Q.ANT, SiPearl, SpiNNcloud and VSORA. But more than their region of origin, what matters to them is that ZML can work with them on “things that have not been done before anywhere in the world.”

That doesn’t mean Morin is bearish on Nvidia. It is not, in part because of its existing offering. He told TechCrunch that ZML has a good relationship with the AI ​​chip giant, which has been preparing for the rise of inference.

Inference has been such an intense area of ​​investment that the trend has been hailed as the “inference gold rush.” So ZML has competition like Baseten, recently valued at $13 billion; Inferact, from the creators of the open source project vLLM; as well as RadixArk, the commercial company behind SGLang.

Both vLLM and SGLang partially compete with LLMD, but Morin’s ambitions for ZML cover a broader spectrum. “We’ve gotten to the point where we’re co-designing silicon,” he said. Additionally, he credited ZML’s small 20-person team for the reason the Paris-based startup has been able to move forward quickly, with more launches in the plans.

It also helped that this small team is well funded for its size. Working as VP of Engineering at Zenly, which Snapchat acquired for nine figures in 2017, Morin raised $20 million from venture firms including Harry Stebbings’ 20VC, >commit, AALVC, Drysdale Ventures, Xavier Niel’s Kima Ventures, Kindred Capital, LocalGlobe, and Puzzle Ventures.

Unlike the first public ZML project, the inference-focused machine learning framework released in 2024 and updated in March, ZML/LLMD is not open source. But it is launched as a free product with the aim of learning about its use. “I prefer to measure and [then generate revenue] where it is most effective without stupidly hindering my growth because I have been too greedy from the beginning,” Morin said.

It is too early to know when ZML/LLMD could become a paid product and what its adoption will look like. But the startup’s cap table confirms that other founders are paying attention, including Dagger and Docker founder Solomon Hykes, Clément Delangue and Julien Chaumond of Hugging Face, as well as LeCun, now with AMI Labs. This also reinforces the argument that European AI startups can now build from home. “I couldn’t do ZML anywhere else but Paris,” Morin said.