llamaperf

M1 16GB vs NVIDIA RTX 4070 for local LLMs

2 reports on the M1 16GB and 9 on the NVIDIA RTX 4070

Which is faster for local LLMs?

On Gemma 4 8B at 4-bit, the M1 16GB runs at 8.2 tokens per second (one run) and the NVIDIA RTX 4070 at 55.0 (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 7.4x higher on the NVIDIA RTX 4070 (68 GB/s on the M1 16GB, 504 on the NVIDIA RTX 4070).

M1 16GB
Memory
16GB unified
Memory bandwidth
68 GB/s
FP16 compute
2.6 TFLOPS
Reports
2
NVIDIA RTX 4070
Memory
12GB
Memory bandwidth
504 GB/s
FP16 compute
116 TFLOPS
Reports
9

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 16GBNVIDIA RTX 4070
Gemma 4 8B4-bit8.2one run55.0one 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 16GBNVIDIA RTX 4070
Gemma 4 8B9.7 t/sQ5_K_M42.6 t/sQ6_K
Qwen3.8 27Bdoesn't fit at these settings1.7 t/sQ8_0, layers on CPU
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 settings13.9 t/sQ6_K, experts in RAM
Qwen3.6 27Bdoesn't fit at these settings1.7 t/sQ8_0, layers on CPU
Gemma 4 26B · 4B activedoesn't fit at these settings82.3 t/sQ2_K

Change the context, RAM or model in the calculator: M1 16GB · NVIDIA RTX 4070

Every report on each card, with its source: M1 16GB · NVIDIA RTX 4070

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