NVIDIA RTX Pro 6000 vs Apple M4 Ultra (192GB)
Side-by-side local AI comparison — VRAM, memory bandwidth, model compatibility, and estimated tokens per second across 70 open-weight models.
Quick verdict
Apple M4 Ultra (192GB) wins for local AI inference. It has 96 GB more VRAM and -19% more memory bandwidth, runs 64 models natively (vs 57), and exclusively fits 7 models the other cannot. Note: NVIDIA RTX Pro 6000 uses CUDA while Apple M4 Ultra (192GB) uses METAL — software ecosystem matters for your framework.
Analysis
The NVIDIA RTX Pro 6000 and Apple M4 Ultra represent two fundamentally different approaches to high-memory local AI inference: a dedicated CUDA workstation GPU that slots into existing x86 infrastructure, versus an integrated Apple Silicon system with a unified memory pool that no discrete GPU at the same price can match in total capacity.
The M4 Ultra's 192 GB of unified LPDDR5X memory at 1,092 GB/s gives it 2× the capacity of the RTX Pro 6000 at roughly 1.7× the price ($9,500–$10,500 for Mac Studio/Mac Pro vs ~$6,300 for the GPU alone). For models that benefit from that extra headroom — running a 70B model at FP16, or keeping multiple 70B models loaded simultaneously — the M4 Ultra is meaningfully better. The RTX Pro 6000's 1,344 GB/s GDDR7 bandwidth edges out the M4 Ultra's 1,092 GB/s, translating to roughly 20% faster token generation for models that fit in 96 GB. The Pro 6000 also plugs into any existing x86 workstation; the M4 Ultra requires committing to Apple's macOS ecosystem and the MLX/Metal software stack.
Bottom line: The M4 Ultra is the better platform if you frequently run 70B models at Q8_0 or FP16, work primarily in Python with MLX, and want a quiet all-in-one desktop. The RTX Pro 6000 is the better choice if you run 70B at Q4–Q6 (where 96 GB is sufficient), need CUDA-native frameworks like PyTorch, vLLM, or TensorRT-LLM, or are expanding an existing NVIDIA workstation setup. Both are serious platforms; the choice is as much about ecosystem and form factor as it is about the numbers.
Specs comparison
| Spec | NVIDIA RTX Pro 6000 | Apple M4 Ultra (192GB) |
|---|---|---|
| VRAM | 96 GB | 192 GB unified |
| Memory type | GDDR7 | LPDDR5X |
| Bandwidth | 1344 GB/s(+23%) | 1092 GB/s |
| CPU cores | — | 32 (24P + 8E) |
| Architecture | Blackwell | Apple M4 Ultra |
| Backend | CUDA | METAL |
| Tier | Workstation | Workstation |
| Released | 2025 | 2025 |
| Models (native) | 57 | 64 |
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 M4 Ultra (192GB) | Delta |
|---|---|---|---|
| Llama 3.3 70B Instruct(70B) | 38.4 t/s(NVFP4) | 7.8 t/s(BF16) | +392% |
| Qwen 3.6 27B(27B) | 24.9 t/s(BF16) | 10.1 t/s(FP32) | +147% |
| Llama 3.1 8B Instruct(8B) | 42 t/s(FP32) | 34.1 t/s(FP32) | +23% |
| Qwen 2.5 7B Instruct(7.6B) | 44.2 t/s(FP32) | 35.9 t/s(FP32) | +23% |
Delta is NVIDIA RTX Pro 6000 relative to Apple M4 Ultra (192GB).
Only NVIDIA RTX Pro 6000 can run(0)
No exclusive models — Apple M4 Ultra (192GB) can run everything NVIDIA RTX Pro 6000 can.
Only Apple M4 Ultra (192GB) can run(7)
Both run natively(57)
These models fit in VRAM on both GPUs. Bandwidth determines which runs them faster.
- Qwen3 235B-A22B (MoE)204.3 t/svs84.8 t/s
- MiniMax M2.5 229B449.4 t/svs186.5 t/s
- MiniMax M2.7 229B449.4 t/svs186.5 t/s
- Mixtral 8x22B Instruct v0.175.8 t/svs30.8 t/s
- Qwen 3.5 122B-A10B (MoE)295.7 t/svs120.1 t/s
- Nemotron 3 Super 120B246.4 t/svs100.1 t/s
- GPT-OSS 120B591.4 t/svs240.2 t/s
- Llama 4 Scout 109B173.9 t/svs70.7 t/s
- GLM-4.5 Air 106B246.4 t/svs100.1 t/s
- GLM-4.6V 106B246.4 t/svs100.1 t/s
- Qwen 2.5 72B Instruct37.3 t/svs7.6 t/s
- Llama 3.3 70B Instruct38.4 t/svs7.8 t/s
- DeepSeek R1 Distill Llama 70B38.4 t/svs7.8 t/s
- Llama 3.1 70B Instruct38.4 t/svs7.8 t/s
- Mixtral 8x7B Instruct v0.1229.2 t/svs46.6 t/s
- Command-R 35B19.2 t/svs7.8 t/s
- +41 more on both
Which should you choose?
- • Faster token generation is the priority
- • You rely on CUDA-based tools (PyTorch, vLLM, Ollama)
- • You need to run larger models (>96 GB VRAM)
- • 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 M4 Ultra (192GB)?
- For local AI inference, the Apple M4 Ultra (192GB) has the edge. It offers 192 GB VRAM (vs 96 GB) and 1092 GB/s bandwidth (vs 1344 GB/s), letting it run 64 models natively in VRAM vs 57 for its rival.
- How much VRAM does the NVIDIA RTX Pro 6000 have vs the Apple M4 Ultra (192GB)?
- The NVIDIA RTX Pro 6000 has 96 GB of GDDR7 at 1344 GB/s. The Apple M4 Ultra (192GB) has 192 GB of LPDDR5X at 1092 GB/s. The Apple M4 Ultra (192GB) has 96 GB more VRAM, allowing it to run 7 models the NVIDIA RTX Pro 6000 cannot fit natively.
- 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 38.4 tokens per second.
- Can the Apple M4 Ultra (192GB) run Llama 3.3 70B?
- Yes. The Apple M4 Ultra (192GB) runs Llama 3.3 70B natively at BF16 quantization at approximately 7.8 tokens per second.
- What is the difference between the NVIDIA RTX Pro 6000 and Apple M4 Ultra (192GB) 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 M4 Ultra (192GB) has 192 GB VRAM at 1092 GB/s (METAL backend). VRAM determines which models fit; bandwidth determines tokens per second. The NVIDIA RTX Pro 6000 runs 57 models natively vs 64 for the Apple M4 Ultra (192GB).