llamaperf

NVIDIA DGX Spark vs M4 16GB for local LLMs

57 reports on the NVIDIA DGX Spark and 5 on the M4 16GB

Which is faster for local LLMs?

On Gemma 4 26B · 4B active at 4-bit, the NVIDIA DGX Spark runs at 69.9 tokens per second (one run) and the M4 16GB at 30.9 (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 2.3x higher on the NVIDIA DGX Spark (273 GB/s on the NVIDIA DGX Spark, 120 on the M4 16GB).

NVIDIA DGX Spark
Memory
128GB unified
Memory bandwidth
273 GB/s
FP16 compute
125 TFLOPS
Reports
57
M4 16GB
Memory
16GB unified
Memory bandwidth
120 GB/s
FP16 compute
4.3 TFLOPS
Reports
5

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

ModelNVIDIA DGX SparkM4 16GB
Gemma 4 26B · 4B active4-bit69.9one run30.9one 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.

ModelNVIDIA DGX SparkM4 16GB
Gemma 4 26B · 4B active10.8 t/sFP16doesn't fit at these settings
Qwen3.8 27B4.0 t/sFP16doesn't fit at these settings
Qwen3.8 125B · 6B active14.4 t/sQ6_Kdoesn't fit at these settings
DeepSeek V4 Flash 284B · 13B active38.3 t/sQ2_Kdoesn't fit at these settings
Qwen3.6 35B · 3B active14.5 t/sFP16doesn't fit at these settings
Qwen3.6 27B4.0 t/sFP16doesn't fit at these settings
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: NVIDIA DGX Spark · M4 16GB

Every report on each card, with its source: NVIDIA DGX Spark · M4 16GB

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