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Apple M2 vs Apple M3 for local AI

Both fit the same models we track, up to gpt-oss 20B. They generate at about the same speed, since their memory bandwidth is close (100 and 100 GB/s).

The numbers that matter

Memory and bandwidth

Memory decides which models fit. Bandwidth decides how fast they generate, because every token reads the model's active weights once.

Apple M2Apple M3
Memory8 or 16 or 24 GB unified memory8 or 16 or 24 GB unified memory
Usable for modelsAbout 16 GB of 24 GBAbout 16 GB of 24 GB
Bandwidth100 GB/s100 GB/s
Biggest model that fitsgpt-oss 20Bgpt-oss 20B
Best everyday pickQwen3 4B, ~22 tok/sQwen3 4B, ~22 tok/s
Model by model

Every model, side by side

Estimated generation speed at the largest memory size each was sold in (24 GB and 24 GB).

ModelApple M2Apple M3
Qwen3 4B
About 4 GB
~22 tok/s Usable~22 tok/s Usable
Qwen3 8B
About 7 GB
~11 tok/s Usable~11 tok/s Usable
Gemma 3 12B
About 10 GB
~7 tok/s Slow~7 tok/s Slow
Qwen3 14B
About 11 GB
~6 tok/s Slow~6 tok/s Slow
gpt-oss 20B
About 16 GB
~18 tok/s Usable~18 tok/s Usable
Gemma 3 27B
About 19 GB
Needs 19 GBNeeds 19 GB
Qwen3 30B-A3B
About 21 GB
Needs 21 GBNeeds 21 GB
Qwen3 32B
About 22 GB
Needs 22 GBNeeds 22 GB
Llama 3.3 70B
About 45 GB
Needs 45 GBNeeds 45 GB
gpt-oss 120B
About 67 GB
Needs 67 GBNeeds 67 GB
Qwen3 235B-A22B
About 144 GB
Needs 144 GBNeeds 144 GB
DeepSeek R1 671B
About 406 GB
Needs 406 GBNeeds 406 GB
Which should you get?

They're close for local AI, so pick on price. Price matters as much as either number: check used prices on the builds page, where every parts list has a measured or estimated speed.

How we estimate

Both columns use the same formula: memory bandwidth divided by the weights read per token, calibrated on our own rack, with Ollama's default 4-bit sizes. Real results are usually within about a third. Measured either one? Send us the number.

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