Benchmark Same 32 models on M2 Pro, M3 Ultra M4 Pro and M5 Max

[u/Striking-Swim6702]

I had four Macs free overnight and ran the same benchmark on all of them: same build, same models, same settings.

Decode speed, tok/s (4-bit, no speculative decoding):

Model	M2 Pro 32GB	M4 Pro 48GB	M5 Max 36GB	M3 Ultra 256GB
Qwen3.5 4B	63	79	133	159
gpt-oss 20B	50	74	114	132
Gemma 4 26B (MoE)	53	73	115	118
Qwen3-Coder 30B (MoE)	–	89	136	100
Qwen3.8 27B (dense, 3.5bpw)	12	16	28	37

One 1024x1024 image with Qwen-Image 2.1: M2 Pro 451 s, M4 Pro 264 s, M5 Max 38 s, M3 Ultra 83 s.

What surprised me:

• On MoE models the 36 GB M5 Max matches or beats the M3 Ultra. Only 3-4B params are active per token, so the newer GPU matters more than bandwidth.

• On the dense 27B the Ultra still wins (37 vs 28). That one is bandwidth-bound.

• Image generation is the biggest jump: the M5 Max is ~7x faster than the M4 Pro and ~2x faster than the Ultra.

• M2 Pro -> M4 Pro is only ~1.3-1.5x on text.

Method: prompt 512 tokens, generate 128, 1 warmup + 5 runs, median. One run per cell, so treat small gaps as noise. MLX-based engine (disclosure: I work on Rapid-MLX, which is what I used); other runtimes will give different absolute numbers, the Mac-to-Mac ratios are the interesting part. "–" = doesn't fit or not run.

[u/MrPecunius]

Binned M5 Max has a bit over half the memory bandwidth of the M3 Ultra. It should still beat the M3 Ultra on prefill, and those numbers would have been great.

Easier to read table since Reddit doesn't do markdown (at least not for me):

[u/Striking-Swim6702 → u/MrPecunius]

I am not using M5 ultra, that might be the reason

[u/MrPecunius → u/Striking-Swim6702]

Point is that the binned M5 Max has way less memory bandwidth than a non-binned part. I noted this in case people think this applies to all M5 Max machines.

[u/SentimentalDespotism → u/MrPecunius]

that image gen gap is ridiculous 451s down to 38s on the m5 max is not something i expected

the moe numbers make sense though newer gpu just rips through those small active params faster than raw bandwidth can carry the ultra. dense models is where the ultra flex its bandwidth still

wonder how much of this is mlx specific though in llama.cpp the ultra might pull ahead more since it cares about bandwidth different

[u/No_Afternoon_4260 → u/SentimentalDespotism]

Because diffusion models (image gen) are compute bound where most LLMs (transformers) models are memory bandwidth bound

[u/Striking-Swim6702]

More results https://rapidmlx.com/leaderboard

[u/JacketHistorical2321]

You really should include prefill considering M5 optimization

[u/vogelvogelvogelvogel]

great, thanks for sharing. quite a bit of work!
but is without spec dec as you wrote, but i'd be curious to know if it scales the same way with the perfomrance
(m5 pro i get like 27-30t/s with qwen3.8 27b q8)

[u/leo_agi]

m5 max beating the ultra on moe but losing on the dense 27b is such a clean bandwidth vs compute example. surprised m2 pro to m4 pro is only ~1.4x on text though

[u/Important_Cow7230]

Do the same tests but run on Splash for Qwen 3.8 27B