NVIDIA DGX Spark (128GB) vs Apple M3 Ultra (96GB)
Side-by-side local AI comparison — VRAM, memory bandwidth, model compatibility, and estimated tokens per second across 84 open-weight models.
Quick verdict
NVIDIA DGX Spark (128GB) wins for local AI inference. It has 32 GB more VRAM and -67% more memory bandwidth, runs 64 models natively (vs 61), and exclusively fits 3 models the other cannot. Note: NVIDIA DGX Spark (128GB) uses CUDA while Apple M3 Ultra (96GB) uses METAL — software ecosystem matters for your framework.
Specs comparison
| Spec | NVIDIA DGX Spark (128GB) | Apple M3 Ultra (96GB) |
|---|---|---|
| VRAM | 128 GB unified | 96 GB unified |
| Memory type | LPDDR5X | LPDDR5X |
| Bandwidth | 273 GB/s | 819 GB/s(+200%) |
| CPU cores | 20 (10 Cortex-X925 + 10 Cortex-A725) | 28 (20P + 8E) |
| Architecture | Grace Blackwell | Apple M3 Ultra |
| Backend | CUDA | METAL |
| Tier | Workstation | Workstation |
| Released | 2025 | 2025 |
| Models (native) | 64 | 61 |
Estimated tokens per second
Computed from memory bandwidth and model active-parameter weight. Assumes model fits natively in VRAM.
| Model | NVIDIA DGX Spark (128GB) | Apple M3 Ultra (96GB) | Delta |
|---|---|---|---|
| Llama 3.3 70B Instruct(70B) | 4.7 t/s(NVFP4) | 8.5 t/s(Q8_0) | -45% |
| Qwen 3.6 27B(27B) | 3.3 t/s(BF16) | 12 t/s(BF16) | -73% |
| Llama 3.1 8B Instruct(8B) | 5.4 t/s(FP32) | 19.8 t/s(FP32) | -73% |
| Qwen 2.5 7B Instruct(7.6B) | 5.7 t/s(FP32) | 21.2 t/s(FP32) | -73% |
Delta is NVIDIA DGX Spark (128GB) relative to Apple M3 Ultra (96GB).
Only NVIDIA DGX Spark (128GB) can run(3)
Only Apple M3 Ultra (96GB) can run(0)
No exclusive models — NVIDIA DGX Spark (128GB) can run everything Apple M3 Ultra (96GB) can.
Both run natively(61)
These models fit in VRAM on both GPUs. Bandwidth determines which runs them faster.
- Step 3.7 Flash8.9 t/svs42.3 t/s
- Step 3.5 Flash8.9 t/svs42.3 t/s
- Mixtral 8x22B Instruct v0.12.7 t/svs10.2 t/s
- Mistral Medium 3.5 128B2.7 t/svs10.2 t/s
- Qwen 3.5 122B-A10B (MoE)9.4 t/svs29.2 t/s
- Nemotron 3 Super 120B8.6 t/svs26.1 t/s
- GPT-OSS 120B20.1 t/svs61.5 t/s
- Llama 4 Scout 109B5.7 t/svs17.6 t/s
- GLM-4.5 Air 106B8.2 t/svs21.8 t/s
- GLM-4.6V 106B8.2 t/svs21.8 t/s
- Qwen 2.5 72B Instruct4.6 t/svs10.6 t/s
- Llama 3.3 70B Instruct4.7 t/svs8.5 t/s
- DeepSeek R1 Distill Llama 70B4.7 t/svs8.5 t/s
- Llama 3.1 70B Instruct4.7 t/svs8.5 t/s
- Mixtral 8x7B Instruct v0.12 t/svs14 t/s
- Command-R 35B2.2 t/svs13.7 t/s
- +45 more on both
Which should you choose?
Choose NVIDIA DGX Spark (128GB) if:
- • You need to run larger models (>96 GB VRAM)
- • You rely on CUDA-based tools (PyTorch, vLLM, Ollama)
Choose Apple M3 Ultra (96GB) if:
- • Faster token generation is the priority
- • You're on macOS and want native Metal acceleration (MLX, llama.cpp)
Frequently asked questions
- Which is better for local AI, the NVIDIA DGX Spark (128GB) or Apple M3 Ultra (96GB)?
- For local AI inference, the NVIDIA DGX Spark (128GB) has the edge. It offers 128 GB VRAM (vs 96 GB) and 273 GB/s bandwidth (vs 819 GB/s), letting it run 64 models natively in VRAM vs 61 for its rival.
- How much VRAM does the NVIDIA DGX Spark (128GB) have vs the Apple M3 Ultra (96GB)?
- The NVIDIA DGX Spark (128GB) has 128 GB of LPDDR5X at 273 GB/s. The Apple M3 Ultra (96GB) has 96 GB of LPDDR5X at 819 GB/s. The NVIDIA DGX Spark (128GB) has 32 GB more VRAM, allowing it to run 3 models the Apple M3 Ultra (96GB) cannot fit natively.
- Can the NVIDIA DGX Spark (128GB) run Llama 3.3 70B?
- Yes. The NVIDIA DGX Spark (128GB) runs Llama 3.3 70B natively at NVFP4 quantization at approximately 4.7 tokens per second.
- Can the Apple M3 Ultra (96GB) run Llama 3.3 70B?
- Yes. The Apple M3 Ultra (96GB) runs Llama 3.3 70B natively at Q8_0 quantization at approximately 8.5 tokens per second.
- What is the difference between the NVIDIA DGX Spark (128GB) and Apple M3 Ultra (96GB) for AI?
- The key difference for AI inference is VRAM and memory bandwidth. The NVIDIA DGX Spark (128GB) has 128 GB VRAM at 273 GB/s (CUDA backend). The Apple M3 Ultra (96GB) has 96 GB VRAM at 819 GB/s (METAL backend). VRAM determines which models fit; bandwidth determines tokens per second. The NVIDIA DGX Spark (128GB) runs 64 models natively vs 61 for the Apple M3 Ultra (96GB).