NVIDIA RTX Pro 6000 vs Apple M3 Ultra (96GB)
Side-by-side local AI comparison: VRAM, memory bandwidth, model compatibility, and estimated tokens per second across 110 open-weight models.
Quick verdict
NVIDIA RTX Pro 6000 is faster for local AI inference, but only on memory bandwidth (118.8% more). Both GPUs have 96 GB VRAM and run the exact same 77 models natively; neither fits anything the other can't. Note: NVIDIA RTX Pro 6000 uses CUDA while Apple M3 Ultra (96GB) uses METAL; software ecosystem matters for your framework.
Analysis
The Apple M3 Ultra (96 GB) and NVIDIA RTX Pro 6000 share identical VRAM capacity, a striking coincidence that makes this one of the most direct cross-platform comparisons possible. Both can hold a 70B model at Q4_K_M without CPU offload. What separates them is bandwidth, price, and form factor.
At 1,792 GB/s, the RTX Pro 6000 delivers about 2.2× the memory bandwidth of the M3 Ultra's own considerable 819 GB/s, even though the M3 Ultra is two fused M3 Max dies rather than a single laptop-class chip. Since memory bandwidth is the primary determinant of tokens per second for LLM inference, the Pro 6000 generates tokens meaningfully faster on any model both platforms can hold; this site's calculator puts the gap at about 1.8× on Qwen 3.6 27B at Q4_K_M (68.5 versus 38.6 tok/s), narrower than the bandwidth ratio because Apple Silicon tracks closer to its bandwidth ceiling. The M3 Ultra 96 GB starts at $3,999 in the Mac Studio and runs on Apple's efficient ARM architecture with MLX, offering substantially better power efficiency and a silent, compact desktop form factor. The RTX Pro 6000, at roughly $8,565 at launch as a standalone card, requires an existing x86 workstation and carries dramatically higher power draw.
Bottom line: For a quiet desktop workstation that can run 70B models locally at a lower total cost, the M3 Ultra Mac Studio is hard to beat. For a machine where throughput per second matters most (serving API requests, running evaluation suites, batch processing prompts), the RTX Pro 6000 wins on speed, by nearly 1.8× on the same model and quant. If tokens-per-second on 70B-class models is the primary metric, the RTX Pro 6000 is the faster card at the same VRAM.
Specs comparison
| Spec | NVIDIA RTX Pro 6000 | Apple M3 Ultra (96GB) |
|---|---|---|
| VRAM | 96 GB | 96 GB unified |
| Memory type | GDDR7 | LPDDR5X |
| Bandwidth | 1792 GB/s(+119%) | 819 GB/s |
| CPU cores | N/A | 28 (20P + 8E) |
| Architecture | Blackwell | Apple M3 Ultra |
| Backend | CUDA | METAL |
| Tier | Workstation | Workstation |
| Released | 2025 | 2025 |
| Models (native) | 77 | 77 |
Estimated tokens per second
Computed from memory bandwidth and model active-parameter weight. Assumes model fits natively in VRAM.
| Model | NVIDIA RTX Pro 6000 | Apple M3 Ultra (96GB) | Delta |
|---|---|---|---|
| Llama 3.3 70B Instruct(70B) | 34.8 t/s(NVFP4) | 8.5 t/s(Q8_0) | +119%at Q8_0 |
| Qwen 3.6 27B(27B) | 26 t/s(BF16) | 12 t/s(BF16) | +117% |
| Llama 3.1 8B Instruct(8B) | 78.4 t/s(BF16) | 38.4 t/s(BF16) | +104% |
| Qwen 2.5 7B Instruct(7.6B) | 86.1 t/s(BF16) | 41.8 t/s(BF16) | +106% |
Delta is NVIDIA RTX Pro 6000 relative to Apple M3 Ultra (96GB), compared at the highest quantization that fits natively on both cards.
Only NVIDIA RTX Pro 6000 can run(0)
No exclusive models: Apple M3 Ultra (96GB) can run everything NVIDIA RTX Pro 6000 can.
Only Apple M3 Ultra (96GB) can run(0)
No exclusive models: NVIDIA RTX Pro 6000 can run everything Apple M3 Ultra (96GB) can.
Both run natively(77)
These models fit in VRAM on both GPUs. Bandwidth determines which runs them faster.
- Step 3.7 Flash136.9 t/svs53.8 t/s
- Step 3.5 Flash136.9 t/svs53.8 t/s
- Qwen3.8-Flash-Next181.4 t/svs69.7 t/s
- Mixtral 8x22B Instruct v0.155.1 t/svs22.8 t/s
- Mistral Medium 3.5 128B20.9 t/svs10.2 t/s
- Ornith 1.5 122B (MoE)124.8 t/svs49.6 t/s
- Qwen 3.5 122B-A10B (MoE)138.2 t/svs49.6 t/s
- Nemotron 3 Super 120B81 t/svs33.6 t/s
- Nemotron 3.5 Super VL 120B-A12B83.2 t/svs32.7 t/s
- GPT-OSS 120B205 t/svs76.2 t/s
- Llama 4 Scout 109B73.2 t/svs26.7 t/s
- GLM-4.5 Air 106B116.9 t/svs39.2 t/s
- GLM-4.6V 106B116.9 t/svs39.2 t/s
- Qwen 2.5 72B Instruct34.1 t/svs10.6 t/s
- Llama 3.3 70B Instruct34.8 t/svs8.5 t/s
- DeepSeek R1 Distill Llama 70B34.8 t/svs8.5 t/s
- +61 more on both
Which should you choose?
- • Faster token generation is the priority
- • You rely on CUDA-based tools (PyTorch, vLLM, Ollama)
- • You're on macOS and want native Metal acceleration (MLX, llama.cpp)
- • Unified memory matters (CPU/GPU share the same pool, no data copy overhead)
Frequently asked questions
- Which is better for local AI, the NVIDIA RTX Pro 6000 or Apple M3 Ultra (96GB)?
- For local AI inference, the NVIDIA RTX Pro 6000 has the edge. It offers 96 GB VRAM (vs 96 GB) and 1792 GB/s bandwidth (vs 819 GB/s), letting it run 77 models natively in VRAM vs 77 for its rival.
- How much VRAM does the NVIDIA RTX Pro 6000 have vs the Apple M3 Ultra (96GB)?
- The NVIDIA RTX Pro 6000 has 96 GB of GDDR7 at 1792 GB/s. The Apple M3 Ultra (96GB) has 96 GB of LPDDR5X at 819 GB/s. Both GPUs have the same VRAM amount; bandwidth determines which generates tokens faster.
- Can the NVIDIA RTX Pro 6000 run Llama 3.3 70B?
- Yes. The NVIDIA RTX Pro 6000 runs Llama 3.3 70B natively at NVFP4 quantization at approximately 34.8 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 RTX Pro 6000 and Apple M3 Ultra (96GB) for AI?
- The key difference for AI inference is VRAM and memory bandwidth. The NVIDIA RTX Pro 6000 has 96 GB VRAM at 1792 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 RTX Pro 6000 runs 77 models natively vs 77 for the Apple M3 Ultra (96GB).