llamaperf

M5 Ultra 256GB vs NVIDIA RTX 4090 for local LLMs

12 reports on the M5 Ultra 256GB and 36 on the NVIDIA RTX 4090

Which is faster for local LLMs?

On Qwen3.8 27B at 4-bit, the M5 Ultra 256GB runs at 50.0 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 22% higher on the M5 Ultra 256GB (1,229 GB/s on the M5 Ultra 256GB, 1,008 on the NVIDIA RTX 4090).

M5 Ultra 256GB
Memory
256GB unified
Memory bandwidth
1,229 GB/s
FP16 compute
not on record
Reports
12
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

ModelM5 Ultra 256GBNVIDIA RTX 4090
Qwen3.8 27B4-bit50.0one 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.

ModelM5 Ultra 256GBNVIDIA RTX 4090
Qwen3.8 27B14.9 t/sFP1636.5 t/sQ4_K_M
Qwen3.8 125B · 6B active50.8 t/sQ8_019.6 t/sQ2_K, experts in RAM
DeepSeek V4 Flash 284B · 13B active66.9 t/sQ4_K_Mdoesn't fit at these settings
Qwen3.6 35B · 3B active53.5 t/sFP16129.3 t/sQ4_K_S
Qwen3.6 27B14.9 t/sFP1636.5 t/sQ4_K_M
Gemma 4 26B · 4B active40.1 t/sFP1682.3 t/sQ5_K_S
DeepSeek V4.1 Flash 552B · 16B active89.4 t/sQ2_Kdoesn't fit at these settings

Change the context, RAM or model in the calculator: M5 Ultra 256GB · NVIDIA RTX 4090

Every report on each card, with its source: M5 Ultra 256GB · NVIDIA RTX 4090

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