alt Hacker NewsMaybe is a bit more niche, but I'm pretty sure there is plenty of people who have enough RAM to run dense models like this one, I don't know how slow is slow but people are willing to sacrifice speed if it means they get a good model running locally.
Look at "SC117/Qwen3.8-Flash-Next-GSQ-RCO-abliterated-GGUF" on HuggingFace, It says it has 3.5M downloads this month and its offering a model that is 2-bit and 3-bit quantized (66GB - 83.9GB).
If this gets quantized to 4-bit (Q4_0) which usually there is very little loss in intelligence compared to the base model, then the model will probably be around 40GB, and looking that people are downloading a model that is at least 20GB bigger, then it might allow even more people to run a bigger model.
But it will only be popular among hobbyist if its as good as it claims to be.
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I hear what you're saying - there are people who will use it even at 0.1 tokens/s and run tasks over night or over multiple days if it is good. but Qwen3.8 Flash Next is a MoE model (6B active) so the memory bandwidth/compute required to calculate each token is very different from a 65B (meaning, 65B all active) dense model!
Qwen 3.8 Flash Next is a mixture of experts model though. It has 6B active parameters per token, so you can cache large parts of the model in RAM and move it in and out of VRAM as needed. If you do that with a dense model it gets much slower.