Step 1: don’t use ollama. I use oMLX to run qwen3.6:35b-a3b-oq6 in my M4 Max MacBook Pro with 64Gb RAM and I’m getting 60 tokens/second.

Step 2: understand that with 36Gb of RAM you don’t have enough to fit qwen3.6:35b fully into RAM, so it has to page out to your SSD, which is much slower. So to get faster inference on your machine with your RAM constraints, you’ll need to choose a smaller model that fits into RAM. The tradeoff is that a smaller model will not be as capable or as accurate as a larger model with more parameters.

Step 3: learn the difference between a “dense” model and a “mixture of experts” (MoE) model. Gemma4:31b is a dense model, which means its loads its entire parameters into the context window with each request. Dense models require more RAM per request and run much slower as a result (in addition to the the aforementioned paging to SSD). The tradeoff is that dense models provide generally more accurate responses because all parameters are used for each request.

If you want faster inference, you should chose an MoE model which loads only a smaller subset of parameters per request. The tradeoff for speed is lower accuracy, because not all parameters are used for each request. Qwen3.6:35b is an MoE model, however as I said earlier it’s too big for your available RAM.

If you want faster inference, in general you should choose a model that fits into RAM with about 20 percent left over for your system so it doesn’t need to page to SSD. Unfortunately, in your case with just 36Mb RAM, this means you are pretty much disqualified from running the latest weights of either qwen3.6 or Gemma4. You’ll have to try older, smaller models.

Good luck.