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 84 open-weight models.
Quick verdict
NVIDIA RTX Pro 6000 wins for local AI inference. It has 64% more memory bandwidth, runs 61 models natively (vs 61), and exclusively fits 0 models the other cannot. 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 1,344 GB/s, the RTX Pro 6000 still delivers about 1.6× the memory bandwidth of the M3 Ultra's own considerable 819 GB/s — the smallest gap of any Apple-vs-NVIDIA comparison on this site, since 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 still generates tokens meaningfully faster on any model both platforms can hold, just not by the 2×+ margin Apple's laptop chips typically trail by. 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 ~$6,300 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, though by a narrower margin here than against Apple's laptop-class Max chips. If tokens-per-second on 70B-class models is the primary metric, the RTX Pro 6000 is still faster despite the same VRAM, just not dramatically so.
Specs comparison
| Spec | NVIDIA RTX Pro 6000 | Apple M3 Ultra (96GB) |
|---|---|---|
| VRAM | 96 GB | 96 GB unified |
| Memory type | GDDR7 | LPDDR5X |
| Bandwidth | 1344 GB/s(+64%) | 819 GB/s |
| CPU cores | — | 28 (20P + 8E) |
| Architecture | Blackwell | Apple M3 Ultra |
| Backend | CUDA | METAL |
| Tier | Workstation | Workstation |
| Released | 2025 | 2025 |
| Models (native) | 61 | 61 |
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) | 23.2 t/s(NVFP4) | 8.5 t/s(Q8_0) | +173% |
| Qwen 3.6 27B(27B) | 16 t/s(BF16) | 12 t/s(BF16) | +33% |
| Llama 3.1 8B Instruct(8B) | 26.4 t/s(FP32) | 19.8 t/s(FP32) | +33% |
| Qwen 2.5 7B Instruct(7.6B) | 28.3 t/s(FP32) | 21.2 t/s(FP32) | +33% |
Delta is NVIDIA RTX Pro 6000 relative to Apple M3 Ultra (96GB).
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(61)
These models fit in VRAM on both GPUs. Bandwidth determines which runs them faster.
- Step 3.7 Flash56.4 t/svs42.3 t/s
- Step 3.5 Flash56.4 t/svs42.3 t/s
- Mixtral 8x22B Instruct v0.113.1 t/svs10.2 t/s
- Mistral Medium 3.5 128B13 t/svs10.2 t/s
- Qwen 3.5 122B-A10B (MoE)46.4 t/svs29.2 t/s
- Nemotron 3 Super 120B42.1 t/svs26.1 t/s
- GPT-OSS 120B99.2 t/svs61.5 t/s
- Llama 4 Scout 109B28.2 t/svs17.6 t/s
- GLM-4.5 Air 106B40.6 t/svs21.8 t/s
- GLM-4.6V 106B40.6 t/svs21.8 t/s
- Qwen 2.5 72B Instruct22.6 t/svs10.6 t/s
- Llama 3.3 70B Instruct23.2 t/svs8.5 t/s
- DeepSeek R1 Distill Llama 70B23.2 t/svs8.5 t/s
- Llama 3.1 70B Instruct23.2 t/svs8.5 t/s
- Mixtral 8x7B Instruct v0.138.7 t/svs14 t/s
- Command-R 35B10.8 t/svs13.7 t/s
- +45 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 1344 GB/s bandwidth (vs 819 GB/s), letting it run 61 models natively in VRAM vs 61 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 1344 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 23.2 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 1344 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 61 models natively vs 61 for the Apple M3 Ultra (96GB).