llamaperf

M4 16GB vs M6 32GB for local LLMs

5 reports on the M4 16GB and 2 on the M6 32GB

Which is faster for local LLMs?

On Gemma 4 26B · 4B active at 4-bit, the M4 16GB runs at 30.9 tokens per second (one run) and the M6 32GB at 52.2 (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 42% higher on the M6 32GB (120 GB/s on the M4 16GB, 171 on the M6 32GB).

M4 16GB
Memory
16GB unified
Memory bandwidth
120 GB/s
FP16 compute
4.3 TFLOPS
Reports
5
M6 32GB
Memory
32GB unified
Memory bandwidth
171 GB/s
FP16 compute
not on record
Reports
2

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

ModelM4 16GBM6 32GB
Gemma 4 26B · 4B active4-bit30.9one run52.2one 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.

ModelM4 16GBM6 32GB
Gemma 4 26B · 4B activedoesn't fit at these settings23.2 t/sQ4_K_M
Qwen3.8 27Bdoesn't fit at these settings10.5 t/sQ4_K_S
Qwen3.8 125B · 6B activedoesn't fit at these settingsdoesn't fit at these settings
DeepSeek V4 Flash 284B · 13B activedoesn't fit at these settingsdoesn't fit at these settings
Qwen3.6 35B · 3B activedoesn't fit at these settings38.2 t/sQ3_K_M
Qwen3.6 27Bdoesn't fit at these settings10.5 t/sQ4_K_S
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: M4 16GB · M6 32GB

Every report on each card, with its source: M4 16GB · M6 32GB

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