Mercury: Ultra-fast language models based on diffusion

Damn, that is fast. But it is faster than I can read, so hopefully they can use that speed and turn it into better quality of the output. Because otherwise, I honestly don't see the advantage, in practical terms, over existing LLMs. It's like having a TV with a 200Hz refresh rate, where 100Hz is just fine.

There are plenty of LLM use cases where the output isn’t meant to be read by a human at all. e.g:

parsing unstructured text into structured formats like JSON

translating between natural or programming languages

serving as a reasoning step in agentic systems

So even if it’s “too fast to read,” that speed can still be useful

Sure, but I was talking about the chat interface, sorry if that was not clear.

This lets you do more (potentially a lot more) reasoning steps and tool calls before answering.

You're missing another big advantage is cost. If you can do 1000tok/s on a $2/hr H100 vs 60tok/s on the same hardware, you can price it at 1/40th of the price for the same margin.

You can also slow down the hardware (say, dropping the clock and then voltages) to save huge amounts of power, which should be interesting for embedded applications.

out of curiosity, is anyone here using AI in embedded with experiences to share? I see NPUs and the like popping up more on credit card and buildroot SBCs I get, but with zero documentation or sample scripts for them.