NVIDIA RTX 6000 Ada vs Apple M2 Ultra (192GB)
Side-by-side local AI comparison: VRAM, memory bandwidth, model compatibility, and estimated tokens per second across 99 open-weight models.
Quick verdict
Apple M2 Ultra (192GB) wins for local AI inference. It has 144 GB more VRAM and -16.7% more memory bandwidth, runs 82 models natively (vs 58), and exclusively fits 24 models the other cannot. Note: NVIDIA RTX 6000 Ada uses CUDA while Apple M2 Ultra (192GB) uses METAL; software ecosystem matters for your framework.
Specs comparison
| Spec | NVIDIA RTX 6000 Ada | Apple M2 Ultra (192GB) |
|---|---|---|
| VRAM | 48 GB | 192 GB unified |
| Memory type | GDDR6 | LPDDR5 |
| Bandwidth | 960 GB/s(+20%) | 800 GB/s |
| CPU cores | N/A | 24 (16P + 8E) |
| Architecture | Ada Lovelace | Apple M2 Ultra |
| Backend | CUDA | METAL |
| Tier | Workstation | Workstation |
| Released | 2022 | 2023 |
| Models (native) | 58 | 82 |
Estimated tokens per second
Computed from memory bandwidth and model active-parameter weight. Assumes model fits natively in VRAM.
| Model | NVIDIA RTX 6000 Ada | Apple M2 Ultra (192GB) | Delta |
|---|---|---|---|
| Llama 3.3 70B Instruct(70B) | 17.2 t/s(Q3_K_M) | 4.5 t/s(BF16) | +282% |
| Qwen 3.6 27B(27B) | 21.3 t/s(Q8_0) | 11.7 t/s(BF16) | +82% |
| Llama 3.1 8B Instruct(8B) | 36.5 t/s(BF16) | 37.5 t/s(BF16) | -3% |
| Qwen 2.5 7B Instruct(7.6B) | 39.8 t/s(BF16) | 40.8 t/s(BF16) | -2% |
Delta is NVIDIA RTX 6000 Ada relative to Apple M2 Ultra (192GB).
Only NVIDIA RTX 6000 Ada can run(0)
No exclusive models: Apple M2 Ultra (192GB) can run everything NVIDIA RTX 6000 Ada can.
Only Apple M2 Ultra (192GB) can run(24)
- MiniMax M3428B
- Llama 3.1 405B Instruct405B
- Llama 4 Maverick 400B400B
- Ornith 1.5 397B (MoE)396.8B
- GLM-4.7 358B358B
- GLM-4.5 355B355B
- GLM-4.6 355B355B
- GLM-5.3-Flash 320B320B
- DeepSeek V4 Flash 284B284B
- DeepSeek V4 Flash 0731 284B284B
- Qwen3 235B-A22B (MoE)235B
- MiniMax M2.5 229B229B
- +12 more
Both run natively(58)
These models fit in VRAM on both GPUs. Bandwidth determines which runs them faster.
- Qwen 2.5 72B Instruct16.7 t/svs4.4 t/s
- Llama 3.3 70B Instruct17.2 t/svs4.5 t/s
- DeepSeek R1 Distill Llama 70B17.2 t/svs4.5 t/s
- Llama 3.1 70B Instruct17.2 t/svs4.5 t/s
- Mixtral 8x7B Instruct v0.117.2 t/svs7.4 t/s
- Command-R 35B15.8 t/svs7.9 t/s
- Qwen 3.5 35B-A3B (MoE)57.8 t/svs31.7 t/s
- Qwen 3.6 35B15.9 t/svs8.9 t/s
- Ornith 1.5 35B-A3B (MoE)57.8 t/svs31.7 t/s
- Yi 1.5 34B Chat16.2 t/svs9 t/s
- Qwen3 32B17.2 t/svs9.6 t/s
- Qwen 2.5 32B Instruct17 t/svs9.5 t/s
- Qwen 2.5 Coder 32B Instruct17 t/svs9.5 t/s
- DeepSeek R1 Distill Qwen 32B17 t/svs9.5 t/s
- Nemotron 3 Nano 30B56.4 t/svs31.3 t/s
- Gemma 4 31B18.3 t/svs10.2 t/s
- +42 more on both
Which should you choose?
Choose NVIDIA RTX 6000 Ada if:
- • Faster token generation is the priority
- • You rely on CUDA-based tools (PyTorch, vLLM, Ollama)
Choose Apple M2 Ultra (192GB) if:
- • You need to run larger models (>48 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)
- • You want the newer architecture and longer driver support lifecycle
Frequently asked questions
- Which is better for local AI, the NVIDIA RTX 6000 Ada or Apple M2 Ultra (192GB)?
- For local AI inference, the Apple M2 Ultra (192GB) has the edge. It offers 192 GB VRAM (vs 48 GB) and 800 GB/s bandwidth (vs 960 GB/s), letting it run 82 models natively in VRAM vs 58 for its rival.
- How much VRAM does the NVIDIA RTX 6000 Ada have vs the Apple M2 Ultra (192GB)?
- The NVIDIA RTX 6000 Ada has 48 GB of GDDR6 at 960 GB/s. The Apple M2 Ultra (192GB) has 192 GB of LPDDR5 at 800 GB/s. The Apple M2 Ultra (192GB) has 144 GB more VRAM, allowing it to run 24 models the NVIDIA RTX 6000 Ada cannot fit natively.
- Can the NVIDIA RTX 6000 Ada run Llama 3.3 70B?
- Yes. The NVIDIA RTX 6000 Ada runs Llama 3.3 70B natively at Q3_K_M quantization at approximately 17.2 tokens per second.
- Can the Apple M2 Ultra (192GB) run Llama 3.3 70B?
- Yes. The Apple M2 Ultra (192GB) runs Llama 3.3 70B natively at BF16 quantization at approximately 4.5 tokens per second.
- What is the difference between the NVIDIA RTX 6000 Ada and Apple M2 Ultra (192GB) for AI?
- The key difference for AI inference is VRAM and memory bandwidth. The NVIDIA RTX 6000 Ada has 48 GB VRAM at 960 GB/s (CUDA backend). The Apple M2 Ultra (192GB) has 192 GB VRAM at 800 GB/s (METAL backend). VRAM determines which models fit; bandwidth determines tokens per second. The NVIDIA RTX 6000 Ada runs 58 models natively vs 82 for the Apple M2 Ultra (192GB).