I think gemma-4-26b-a4b and Qwen3.6-35B-A3B show that there's something very interesting about a local model that does mixture-of-experts (which helps a lot with performance) and has in the order of 30 billion parameters.

These models are very capable, and use around 20-30GB of RAM while they are running.

Provided you have 64GB of RAM that leaves space for running other applications at the same time.

▲

chrisweekly 3 hours ago | parent [-]

Obtaining that 64GB RAM is a meaningful obstacle for many.

	▲	frollogaston 6 minutes ago \| parent \| next [-]
		Not just RAM, VRAM, right? Though they're one and the same on the Mac.
	▲	simonw 3 hours ago \| parent \| prev [-]
		I'm still amazed that you can run LLMs of this quality on a machine that costs less than $3,000. I used to assume that anything GPT-4 equivalent or higher would need $30,000+ of server-class hardware. That said... gemma-4-12b-qat is 7.15GB on disk so should run reasonably well in 16GB, that takes it down to MacBook Air territory https://lmstudio.ai/models/google/gemma-4-12b-qat