The main issue is the compatibility gap between current "modern" local runners and older Intel Mac hardware/software versions. I need a lightweight alternative that doesn't have the strict requirements Ollama does.
I've been looking into a few potential workarounds:
-
LM Studio: It usually has better backward compatibility with Intel Macs than Ollama, though RAM is going to be the primary bottleneck here.
-
llama.cpp: This is the gold standard for "running things from scratch." Since it's C++, I can likely compile it directly on my machine, bypassing the MacOS 12 restriction that kills the Ollama installer.
-
GPT4All: Another option that tends to be more lenient with older hardware.
For those who have managed to get a deployment working on 2017-era Airs, how did you handle the memory pressure? I'm guessing only the smallest quantized models (like Phi-3 or TinyLlama) will actually be usable without the system swapping to death.
If anyone has a specific `make`
command or a build flag for llama.cpp
that optimizes for these older Intel integrated graphics, that would be a huge help.
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