llamaperf

M1 Max 32GB vs NVIDIA RTX 4090 for local LLMs

2 reports on the M1 Max 32GB and 36 on the NVIDIA RTX 4090

Which is faster for local LLMs?

On Qwen3.8 27B at 4-bit, the M1 Max 32GB runs at 15.8 tokens per second (one run) and the NVIDIA RTX 4090 at 31.5 (2 runs). 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 2.5x higher on the NVIDIA RTX 4090 (410 GB/s on the M1 Max 32GB, 1,008 on the NVIDIA RTX 4090).

M1 Max 32GB
Memory
32GB unified
Memory bandwidth
410 GB/s
FP16 compute
10.4 TFLOPS
Reports
2
NVIDIA RTX 4090
Memory
24GB
Memory bandwidth
1,008 GB/s
FP16 compute
330 TFLOPS
Reports
36

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 Max 32GBNVIDIA RTX 4090
Qwen3.8 27B4-bit15.8one run31.52 runs

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 Max 32GBNVIDIA RTX 4090
Qwen3.8 27B20.1 t/sQ4_K_S36.5 t/sQ4_K_M
Qwen3.8 125B · 6B activedoesn't fit at these settings19.6 t/sQ2_K, experts in RAM
DeepSeek V4 Flash 284B · 13B activedoesn't fit at these settingsdoesn't fit at these settings
Qwen3.6 35B · 3B active73.2 t/sQ3_K_M129.3 t/sQ4_K_S
Qwen3.6 27B20.1 t/sQ4_K_S36.5 t/sQ4_K_M
Gemma 4 26B · 4B active44.3 t/sQ4_K_M82.3 t/sQ5_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: M1 Max 32GB · NVIDIA RTX 4090

Every report on each card, with its source: M1 Max 32GB · NVIDIA RTX 4090

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