Is the DGX Spark faster than the M4 Max for LLMs?

The M4 Max reads memory about 100% faster than the DGX Spark (546 vs 273 GB/s), so it writes about 100% faster with any model that fits both. In llama.cpp’s measured test the DGX Spark reads a prompt about 3.5× as fast (3,062 vs 886 tokens a second). Reading a prompt depends on GPU compute rather than memory bandwidth, so this sets the wait before the first word with a long document or codebase.

Which can run bigger models, the M4 Max or the DGX Spark?

Both have about 96 GB for models. 47 of 73 popular open models run well on the M4 Max, 39 on the DGX Spark.

Can MLX Node run on the DGX Spark?

No. MLX Node and MLX Harness run on Apple Silicon Macs only, with Apple’s MLX framework.

M4 Max vs DGX Spark for local LLMs: memory, speed, which to buy

Side by side

	M4 Max	DGX Spark
What it is	MacBook Pro (14-inch, M4 Pro or M4 Max, 2024), MacBook Pro (16-inch, 2024), Mac Studio (2025)	A complete small computer
Memory sizes	36, 48, 64, 128 GB	128 GB
Memory bandwidth	410–546 GB/s	273 GB/s
For models, at most	about 96 GB	about 96 GB
Models that run well	47 of 73	39 of 73
Measured: writing	83 tok/s	57 tok/s
Measured: reading a prompt	886 tok/s	3,062 tok/s

Measured rows: each machine’s fastest result in llama.cpp’s public benchmarks with Llama 2 7B Q4_0, writing 128 tokens (tg128) and reading a 512-token prompt (pp512), in tokens a second. The Mac ran Metal, the DGX Spark CUDA. Apple Silicon results · DGX Spark results For models we count about 75% of memory on both, the macOS default, because the DGX Spark also shares one memory pool between its CPU and GPU. How much its GPU may really use depends on its operating system and settings.

Where each one wins

Writing speed. The M4 Max reads memory about 100% faster than the DGX Spark (546 vs 273 GB/s), so it writes about 100% faster with any model that fits both.
Model size. Both have about 96 GB for models. 47 of 73 popular open models run well on the M4 Max, 39 on the DGX Spark.
Reading long prompts. In llama.cpp’s measured test the DGX Spark reads a prompt about 3.5× as fast (3,062 vs 886 tokens a second). Reading a prompt depends on GPU compute rather than memory bandwidth, so this sets the wait before the first word with a long document or codebase.
Software. The DGX Spark runs NVIDIA’s CUDA, which most AI software supports first, including training code and many image and video tools. On a Mac you run models with MLX, llama.cpp’s Metal backend or PyTorch’s MPS backend instead.
Around it. Both are complete computers that keep the model in one memory pool shared by the CPU and GPU.
The Mac. It’s also an everyday computer, and it runs MLX Harness and MLX Node, which are made for Apple Silicon.

Speed with popular models

Tokens a second for one conversation, each chip at its largest memory size (128 GB and 128 GB), with the best version of the model that fits.

Model	M4 Max	DGX Spark
Llama 3.1 8B Instruct8B	29–51 8-bit	14–26 8-bit
gpt-oss-20b21B · 3.6B active	129–230 native 4.25-bit	65–115 native 4.25-bit
Qwen3.6 35B A3B35B · 3B active	77–137 8-bit	38–68 8-bit
Qwen3.8 27B27B	11–20 6-bit	4–8 8-bit
Llama 3.3 70B Instruct70B	3–6 8-bit	2–3 8-bit
gpt-oss-120b117B · 5.1B active	91–162 native 4.25-bit	45–81 native 4.25-bit
Qwen3.5 122B A10B122B · 10B active	44–78 4-bit	22–39 4-bit
DeepSeek V4 Flash 0731284B · 13B active	60–107 2-bit lower quality	30–53 2-bit lower quality

Questions

Is the DGX Spark faster than the M4 Max for LLMs?: The M4 Max reads memory about 100% faster than the DGX Spark (546 vs 273 GB/s), so it writes about 100% faster with any model that fits both. In llama.cpp’s measured test the DGX Spark reads a prompt about 3.5× as fast (3,062 vs 886 tokens a second). Reading a prompt depends on GPU compute rather than memory bandwidth, so this sets the wait before the first word with a long document or codebase.
Which can run bigger models, the M4 Max or the DGX Spark?: Both have about 96 GB for models. 47 of 73 popular open models run well on the M4 Max, 39 on the DGX Spark.
Can MLX Node run on the DGX Spark?: No. MLX Node and MLX Harness run on Apple Silicon Macs only, with Apple’s MLX framework.

More comparisons

The M4 Max for local AI · Compare every model · The best Mac for local LLMs

How it’s worked out

Memory: the model’s parameters at 8, 6, 4, 3 or 2 bits per weight plus MLX’s group scales (half a bit per weight), or its native format, with room for the context on top. By default macOS lets the GPU use about 75% of unified memory. Speed: memory bandwidth divided by the bytes read per token is the ceiling for one conversation; in llama.cpp’s public Apple Silicon benchmarks real runs reach about 45% to 80% of it, which is the range shown. These are estimates, not measurements: measure your own Mac on MLX Bench. Model figures from each official model card, chip figures from Apple’s Tech Specs. Checked 2026-10-19. Same data as mlx.app/compare.

DGX Spark figures: NVIDIA: DGX Spark (specs).