Is there a limit on the number of active parameters in an MoE model?

<!-- SC_OFF --><div class="md"><p>is it possible that a moe model can decide how many billion parameters to activate per token according to the task. eg if qwen 3.6 35b a3b - if a

<!-- SC_OFF --><div class="md"><p>Hi. What do you think is the smallest Open model, as of Spring 2026, that can match/beat OpenAI GPT 4o Mini in the broadest sense possible?</p>

<!-- SC_OFF --><div class="md"><p>Hello, I am trying to better understand how LLMs work. What I don’t understand is does more parameters necessarily mean better results from

<!-- SC_OFF --><div class="md"><p>I need SOTA ai at like 2k TPS with tiny latency so that I can get time to first answer token under 3 seconds for real time replies with full COT

<!-- SC_OFF --><div class="md"><pre><code>There is a finite set of symbols that LLMs can learn from. Of course, the number of possible combinations is enormous, but many of those

<!-- SC_OFF --><div class="md"><p>It's perhaps the model AI model in the world right know, and I just wanted to understand, if you are serving a billion plus requests a day, what

<!-- SC_OFF --><div class="md"><p>When the Qwen3.6-27B came out, lots of GPU poor people here expressed their hopes for 9B model. The same is now happening two levels higher with

<!-- SC_OFF --><div class="md"><p>LLMs keep improving.<br /> Small models are around 3 years behind frontier models.<br /> Do you think we’ll have a model as good as today’s Fable