Apple M4 Max 128GB vs NVIDIA DGX Spark 128GB for Local LLMs
The NVIDIA DGX Spark 128GB holds bigger models (112 GB vs 96.0 GB), and the Apple M4 Max 128GB generates tokens about 114% faster. Below: speeds on seven popular models, the models only one card can run, and our verdict.
Specs that matter for AI
| Apple M4 Max 128GB | NVIDIA DGX Spark 128GB | |
|---|---|---|
| Memory | 128 GB unified | 128 GB unified |
| GPU-usable memory | 96.0 GB | 112 GB |
| Bandwidth | 546 GB/s | 273 GB/s |
| Architecture | Apple Silicon | Grace Blackwell |
| Released | 2024-10 | 2025-10 |
| Llama 3.1 8B Q4_K_M | ~64 tok/s | ~30 tok/s |
Speed and fit by model
Q4_K_M (or native format) with 8K context.
| Model | VRAM | Apple M4 Max 128GB | NVIDIA DGX Spark 128GB |
|---|---|---|---|
| Llama 3.1 8B | 6.3 GB | Fits ~64 tok/s | Fits ~30 tok/s |
| Qwen 3 14B | 10.5 GB | Fits ~36 tok/s | Fits ~17 tok/s |
| gpt-oss-20b | 13.9 GB | Fits ~107 tok/s | Fits ~57 tok/s |
| Qwen 3 30B-A3B | 18.8 GB | Fits ~86 tok/s | Fits ~51 tok/s |
| Qwen 3 32B | 21.4 GB | Fits ~17 tok/s | Fits ~7.8 tok/s |
| Llama 3.3 70B | 43.2 GB | Fits ~8.0 tok/s | Fits ~3.7 tok/s |
| gpt-oss-120b | 62.0 GB | Fits ~83 tok/s | Fits ~46 tok/s |
Verdict
The DGX Spark gives the GPU more of its memory (about 112 vs 96 GB) and runs the full CUDA stack, which suits fine-tuning, vLLM and image tools. The M4 Max has twice the bandwidth (546 vs 273 GB/s), so it generates tokens about twice as fast on the same model. For chat and coding with large MoE models the Mac is faster; for CUDA development, the Spark.
Which should you buy?
Choose the Apple M4 Max 128GB if…
- You want faster generation on large MoE models.
- You want a laptop or a quiet everyday computer.
- You use LM Studio or MLX.
Choose the NVIDIA DGX Spark 128GB if…
- You develop for CUDA and NVIDIA servers.
- You want to fine-tune models locally.
- You want slightly more GPU-usable memory.
Best quality and longest context on each card
The highest-quality quantization that fits fully at 8K context, and the longest context that fits at Q4_K_M with an FP16 cache.
| Model | Apple M4 Max 128GB: best quant | NVIDIA DGX Spark 128GB: best quant | Apple M4 Max 128GB: max context | NVIDIA DGX Spark 128GB: max context |
|---|---|---|---|---|
| Qwen 3.5 9B | FP16 / BF16 | FP16 / BF16 | 256K | 256K |
| Qwen 3 14B | FP16 / BF16 | FP16 / BF16 | 40K | 40K |
| gpt-oss-20b | MXFP4 (official) | MXFP4 (official) | 128K | 128K |
| Qwen 3.6 27B | FP16 / BF16 | FP16 / BF16 | 256K | 256K |
| Qwen 3 32B | FP16 / BF16 | FP16 / BF16 | 40K | 40K |
| Llama 3.3 70B | Q8_0 | Q8_0 | 128K | 128K |
Image generation
Best weight precision for image models with model swapping, at each model's default resolution. Image generation is also limited by compute, so the card with more tensor-core throughput finishes each image faster even when both fit.
| Model | Apple M4 Max 128GB | NVIDIA DGX Spark 128GB |
|---|---|---|
| SDXL 1.0 | FP16 / BF16 | FP16 / BF16 |
| FLUX.1 [dev] | FP16 / BF16 | FP16 / BF16 |
| Qwen-Image | FP16 / BF16 | FP16 / BF16 |
| Wan 2.2 A14B (T2V / I2V) | FP16 / BF16 | FP16 / BF16 |
Models only one card can run
Only Apple M4 Max 128GB
None
Only NVIDIA DGX Spark 128GB
Frequently asked questions
Is the Apple M4 Max 128GB or NVIDIA DGX Spark 128GB better for local LLMs?
The DGX Spark gives the GPU more of its memory (about 112 vs 96 GB) and runs the full CUDA stack, which suits fine-tuning, vLLM and image tools. The M4 Max has twice the bandwidth (546 vs 273 GB/s), so it generates tokens about twice as fast on the same model. For chat and coding with large MoE models the Mac is faster; for CUDA development, the Spark.
Which is faster for LLM inference?
The Apple M4 Max 128GB, by about 114% for token generation on models that fit both (about 64 vs 30 tokens/s on Llama 3.1 8B Q4_K_M).
Which can run bigger models?
The NVIDIA DGX Spark 128GB, with 112 GB usable versus 96.0 GB.