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 110 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 90 models natively (vs 64), and exclusively fits 26 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) | 64 | 90 |
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) | 20.9 t/s(Q3_K_M) | 4.5 t/s(BF16) | +19%at Q3_K_M |
| Qwen 3.6 27B(27B) | 26 t/s(Q8_0) | 11.7 t/s(BF16) | +19%at Q8_0 |
| Llama 3.1 8B Instruct(8B) | 45.2 t/s(BF16) | 37.5 t/s(BF16) | +21% |
| Qwen 2.5 7B Instruct(7.6B) | 49.4 t/s(BF16) | 40.8 t/s(BF16) | +21% |
Delta is NVIDIA RTX 6000 Ada relative to Apple M2 Ultra (192GB), compared at the highest quantization that fits natively on both cards.
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(26)
- 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
- +14 more
Both run natively(64)
These models fit in VRAM on both GPUs. Bandwidth determines which runs them faster.
- Qwen 2.5 72B Instruct20.4 t/svs4.4 t/s
- Llama 3.3 70B Instruct20.9 t/svs4.5 t/s
- DeepSeek R1 Distill Llama 70B20.9 t/svs4.5 t/s
- Llama 3.1 70B Instruct20.9 t/svs4.5 t/s
- Mixtral 8x7B Instruct v0.160.9 t/svs20.4 t/s
- Flint 40B (MoE)147.7 t/svs52.6 t/s
- Command-R 35B20.4 t/svs7.9 t/s
- Qwen 3.5 35B-A3B (MoE)137.9 t/svs54.2 t/s
- Qwen 3.6 35B-A3B (MoE)137.9 t/svs54.2 t/s
- Ornith 1.5 35B-A3B (MoE)137.9 t/svs54.2 t/s
- Yi 1.5 34B Chat20.3 t/svs9 t/s
- Nanbeige4.3 33B21.4 t/svs9.4 t/s
- Qwen3 32B21.4 t/svs9.6 t/s
- Qwen 2.5 32B Instruct21.2 t/svs9.5 t/s
- Qwen 2.5 Coder 32B Instruct21.2 t/svs9.5 t/s
- DeepSeek R1 Distill Qwen 32B21.2 t/svs9.5 t/s
- +48 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 90 models natively in VRAM vs 64 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 26 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 20.9 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 64 models natively vs 90 for the Apple M2 Ultra (192GB).