On a recent episode of the The New Stack Agents, Inception Labs CEO Stefano Ermon introduced Mercury 2, a large language model built on diffusion rather than the standard autoregressive approach. Traditional LLMs generate text token by token from left to right, which Ermon describes as “fancy autocomplete.” In contrast, diffusion models begin with a rough draft and refine it in parallel, similar to image systems like Stable Diffusion.

This parallel process allows Mercury 2 to produce over 1,000 tokens per second—five to ten times faster than optimized models from labs such as OpenAI, Anthropic, and Google, according to company tests. Ermon argues diffusion models better leverage GPUs, with support from investor Nvidia to optimize performance.

While Mercury 2 matches mid-tier models like Claude Haiku and Google Flash rather than top systems such as Claude Opus or GPT-4, Ermon believes diffusion’s speed and economic advantages will become increasingly compelling as AI applications scale.

Learn more from The New Stack about the latest developments around around large language model built on diffusion: 

How Diffusion-Based LLM AI Speeds Up Reasoning

Get Ready for Faster Text Generation With Diffusion LLMs 

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