Here's what's important about this paper. It is written by AMD researchers. It shows AMD is investing in AI research. Is this the same level of achievement as DeepSeek 3.2. Most likely not. Do they have novel ideas? Difficult to say, there are hundreds of new ideas being tried in this space. Is this worthless? Most certainly not. In order to make progress in this domain (as in any other), you first need to get your feet wet. You need to play with the various components, and see how they fit together. The idea in this paper is that you can combine somehow SSMs (like Mamba) and LLMs (like LLama). The examples they give are absolute toys compared to DeepSeek 3.2 (the largest is 8 billion parameters, while DeepSeek 3.2 has 671 billion parameters). The comparison you are trying to make simply does not apply. The good news for all of us is that AMD is working in this space.

Mamba based LLMs aren't even close to novel though. IBM's been doing this since forever [1].

Also, you're off on Deepseek V3.2's param count, the full model's 685B in size with the MTP layer.

I don't think there's anything interesting here other than "I guess AMD put out a research paper", and it's not cutting edge when Deepseek or even IBM is running laps around them.

[1] Here's a news article from April, although IBM has been doing it for a long time before that https://research.ibm.com/blog/bamba-ssm-transformer-model