Which LLMs Fit on a 64 GB Mac?

By TechCompare · Updated · How we check

The calculator gives a 64 GB Mac 56 GB for models and keeps 8 GB for macOS and your apps. 60 of the 108 models fit there at Q4_K_M with 8K context, and Apertus v1.5 70B ranks first at 51.3 GB.

A Mac shares one pool of memory between macOS, your apps and the GPU. That's how a 64 GB Mac holds models no 32 GB or 48 GB graphics card can, like Llama 3.3 70B at Q4_K_M. This page checks every model in the LLM VRAM Calculator against it, at Q4_K_M and Q8_0, with 8K and 32K tokens of context.

The calculator keeps 8 GB for macOS and your apps and checks each model against the other 56 GB. That's a planning estimate, not a limit Apple sets. Each total counts the weights, the KV cache and a 10% allowance for the runtime, and each one links to the calculator with the same setup.

Models that fit on a 64 GB Mac

Best pick first, by size and release date. Each cell is the total memory the model needs. Tight means under 10% of the memory is left. Pick a number to open it in the calculator.

ModelQ4_K_M8K contextQ4_K_M32K contextQ8_08K contextQ8_032K context
Apertus v1.5 70B71.94B, Sep 202651.3 GBTight60.1 GBShort 4.1 GB86.9 GBShort 30.9 GB95.7 GBShort 39.7 GB
Nemotron 3.5 Bolt 74B (MoE)74.89B, 9B active, Oct 202650.5 GBTight50.8 GBTight87.6 GBShort 31.6 GB87.8 GBShort 31.8 GB
Nanbeige4.3 33B33.1B, Oct 202624.2 GBFits29.9 GBFits40.6 GBFits46.3 GBFits
K2 Horizon 32B34.78B, Sep 202625.7 GBFits32.8 GBFits43.0 GBFits50.1 GBFits
Granite Switch 4.1 30B29.99B, Oct 202622.5 GBFits29.6 GBFits37.4 GBFits44.5 GBFits
Muse Glimmer 30B30B, Aug 202620.4 GBFits20.7 GBFits35.2 GBFits35.6 GBFits
Qwen 3.8 27B27.5B, Aug 202619.2 GBFits21.0 GBFits32.9 GBFits34.6 GBFits
Gemma 4 31B (Dense)30.7B, Mar 202622.3 GBFits24.5 GBFits37.5 GBFits39.7 GBFits
Llama 3.3 70B70B, Nov 202450.0 GBFits58.8 GBShort 2.8 GB84.6 GBShort 28.6 GB93.5 GBShort 37.5 GB
Qwen3.5 27B27.78B, Feb 202619.4 GBFits21.2 GBFits33.2 GBFits35.0 GBFits
Qwen 2.5 72B72B, Sep 202451.3 GBTight60.2 GBShort 4.2 GB87.0 GBShort 31.0 GB95.8 GBShort 39.8 GB
K2 Horizon MoVA 36B-A4B (MoE)37.44B, 4B active, Sep 202626.9 GBFits32.3 GBFits45.5 GBFits50.8 GBTight
Flint 40B (MoE)39.8B, 2.5B active, Oct 202627.1 GBFits27.8 GBFits46.8 GBFits47.5 GBFits
Xing4.0 29B-A4B (MoE)31.2B, 4B active, Sep 202621.4 GBFits22.6 GBFits36.8 GBFits38.1 GBFits
Llama 3.1 70B70B, Jul 202450.0 GBFits58.8 GBShort 2.8 GB84.6 GBShort 28.6 GB93.5 GBShort 37.5 GB

Just too big at Q4_K_M with 8K context

Totals include the weights, the KV cache at FP16 with a batch size of 1, and a 10% allowance for the runtime.

What fits at Q4_K_M

This is the size where 70B models fit. Llama 3.3 70B needs 50.0 GB at Q4_K_M with 8K, and Qwen 2.5 72B, the largest dense model that fits, needs 51.3 GB. Apertus v1.5 70B ranks first at 51.3 GB, a tight fit.

The largest model that fits is Nemotron 3.5 Bolt 74B (MoE), at 50.5 GB. It runs about 9B parameters per token, so it writes text much faster than a dense 70B model on the same Mac.

The order follows the model finder's best pick, which weighs size against release date. Treat the top rows as a short list to try, not a ranking of quality.

Long context on a 64 GB Mac

At 32K, the dense 70B models drop out. Llama 3.3 70B grows from 50.0 GB to 58.8 GB, since its KV cache takes 10.8 GB at 32K. A q8_0 KV cache halves that cache and brings it back under 56 GB, at a small cost in quality. You can set it in the calculator.

Nemotron 3.5 Bolt 74B barely changes. It needs 50.8 GB at 32K, because most of its layers keep a small fixed state instead of a KV cache. For long chats on this Mac, it's the big model to try first.

Q8_0 on a 64 GB Mac

At Q8_0, the 30B class fits, even at 32K. K2 Horizon 32B, the largest dense model at Q8_0, needs 43.0 GB at 8K and 50.1 GB at 32K. The 70B models don't fit at Q8_0: Llama 3.3 70B needs 84.6 GB there.

So the choice is a 70B model at Q4_K_M with a short context, or a 30B model at Q8_0 with room for long chats. Nanbeige4.3 33B is a good middle ground, at 46.3 GB with Q8_0 and 32K.

Just out of reach

The next models up are well past 56 GB. Llama 4 Scout needs 75.0 GB at Q4_K_M and still 60.6 GB at Q3_K_M. The 120B mixture of experts models, like gpt-oss 120B at 80.9 GB, are a size class up.

For those, see the 128 GB unified memory list. On a 64 GB Mac, a smaller quant won't close a gap this wide.

Letting the GPU use more memory

macOS doesn't let the GPU use all of a Mac's memory by default, and the default share can be less than this page assumes. On macOS Sonoma and later, you can raise the limit with a terminal command. It lasts until the next restart.

This sets it to the 56 GB this page uses:

bash
sudo sysctl iogpu.wired_limit_mb=57344

Keep an eye on memory pressure in Activity Monitor. If the Mac starts swapping to disk, lower the limit or pick a smaller model.

Other memory sizes

Frequently asked questions

What's the best LLM for a 64 GB Mac?
Apertus v1.5 70B ranks first at Q4_K_M with 8K context, at 51.3 GB. For long chats, Nemotron 3.5 Bolt 74B (MoE) fits even at 32K, at 50.8 GB. Try a few from the top of the table on your own tasks.
Can a 64 GB Mac run Llama 3.3 70B?
Yes, at Q4_K_M with a short context. Llama 3.3 70B needs 50.0 GB with 8K, but 58.8 GB with 32K, which doesn't fit. Close other big apps while it runs.
How much memory does macOS need for itself?
The calculator keeps 8 GB for macOS and your apps and gives the model 56 GB. That's a planning estimate, not a rule from Apple. With a browser and a code editor open, you may want more room, so save the tight rows for when the Mac is otherwise quiet.
Is a 64 GB Mac better than an RTX 5090 for LLMs?
It holds bigger models. The Mac fits a 70B model at Q4_K_M, which the 32 GB list can't. The RTX 5090 has far more memory bandwidth, so for models that fit both, it writes tokens faster and reads long prompts much faster.
Can I let the GPU use more of a Mac's memory?
Yes. On macOS Sonoma and later, the iogpu.wired_limit_mb setting changes how much memory the GPU can use, until the next restart. The command is in the section above. Set it too high and macOS may run short and start swapping.