llamaperf

M1 Pro 32GB vs M2 Max 64GB for local LLMs

2 reports on the M1 Pro 32GB and 4 on the M2 Max 64GB

Which is faster for local LLMs?

On Qwen3.6 35B · 3B active at 4-bit, the M1 Pro 32GB runs at 25.4 tokens per second (one run) and the M2 Max 64GB at 71.3 (one run). Those are community reports whose engines and settings differ, so the gap is a rough sign, not a controlled measure of the two cards. Memory bandwidth, which caps how fast a card can write, is 2.0x higher on the M2 Max 64GB (205 GB/s on the M1 Pro 32GB, 410 on the M2 Max 64GB).

M1 Pro 32GB
Memory
32GB unified
Memory bandwidth
205 GB/s
FP16 compute
5.2 TFLOPS
Reports
2
M2 Max 64GB
Memory
64GB unified
Memory bandwidth
410 GB/s
FP16 compute
13.6 TFLOPS
Reports
4

Measured on both

Median tokens per second from plain runs (one device, one request, no speculative decoding, the whole model in the device's memory) of the same model size at the same quant level. Engines and context lengths can still differ between the runs. How to read these

ModelM1 Pro 32GBM2 Max 64GB
Qwen3.6 35B · 3B active4-bit25.4one run71.3one run

Estimated by the calculator

The quant the calculator recommends for each card and its estimated speed at a 32,768-token context with 32 GB of system RAM, from memory bandwidth and the model's shape. Where a model was also measured above, the measurement wins.

ModelM1 Pro 32GBM2 Max 64GB
Qwen3.6 35B · 3B active40.7 t/sQ3_K_M32.9 t/sQ8_0
Qwen3.8 27B11.2 t/sQ4_K_S10.4 t/sQ8_0
Qwen3.8 125B · 6B activedoesn't fit at these settings47.5 t/sQ2_K
DeepSeek V4 Flash 284B · 13B activedoesn't fit at these settingsdoesn't fit at these settings
Qwen3.6 27B11.2 t/sQ4_K_S10.4 t/sQ8_0
Gemma 4 26B · 4B active24.6 t/sQ4_K_M28.2 t/sQ8_0
DeepSeek V4.1 Flash 552B · 16B activedoesn't fit at these settingsdoesn't fit at these settings

Change the context, RAM or model in the calculator: M1 Pro 32GB · M2 Max 64GB

Every report on each card, with its source: M1 Pro 32GB · M2 Max 64GB

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