64Gb ram mac falls right into the local llm dead zone

Reddit r/LocalLLaMA / 4/2/2026

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Key Points

A Reddit user with an M2 Max Mac (64GB RAM) reports that popular local-LLM model sizes land in a “dead zone,” where systems are too large for consumer hardware but not strong enough to match frontier models.
They compare Qwen3.5 35B (8-bit quant) as fast but “mediocre” for agentic use, versus Qwen3.5 27B (4-bit quant) as better for needed performance but very slow for agent workflows (e.g., up to 10 minutes to generate a folder structure).
They observe a practical gap between mid-sized models (around 35B/27B) and higher-performing options (>100B), implying that 60–70B “sweet spots” with lighter active models don’t seem readily available locally.
The user suggests their hardware and RAM/performance profile aligns uncomfortably with this gap, and they speculate that future techniques like Google’s “turbo quant” could change the situation.
They ask the community for recommendations, implicitly seeking better model/quantization choices or strategies for efficient local deployment on 64GB Macs.

So I recently bought a Mac (m2 max) with local llm use in mind and I did my research and everywhere everyone was saying go for the larger ram option or I will regret it later... So I did.

Time to choose a model:

"Okay, - Nice model, Qwen3.5 35b a3b running 8 bit quant, speedy even with full context size. -> Performance wise it's mediocre especially for more sophisticated agentic use"

"Hmm let me look for better options because I have 64 gbs maybe there is a smarter model out there. - Qwen3.5 27b mlx running at 4 bit quant (also full context size) is just the performance I need since it's a dense model. -> The catch is that, surprise surprise, it's slow so the agent takes up to 10 minutes just to create a folder structure"

So the dream would be like a 70 or 60b with active 9 or 7b model but there is none.

Essentially, they sit in this like awkward middle ground where they are too big for consumer hardware but not powerful enough to compete with those "frontier" giants.

It seems like there really is this gap between the mediocre models (35/27b) and the 'good' ones (>100b) because of that..

And my ram size (and performance) fits exactly into this gap, yippie 👍

But who knows what the future might hold especially with Google's research on turbo quant

what do you guys think or even recommend?

submitted by /u/Skye_sys
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