NVIDIA RTX 4090 vs Apple M2 Ultra (192GB)
Side-by-side local AI comparison — VRAM, memory bandwidth, model compatibility, and estimated tokens per second across 84 open-weight models.
Quick verdict
Apple M2 Ultra (192GB) wins for local AI inference. It has 168 GB more VRAM and -21% more memory bandwidth, runs 71 models natively (vs 46), and exclusively fits 25 models the other cannot. Note: NVIDIA RTX 4090 uses CUDA while Apple M2 Ultra (192GB) uses METAL — software ecosystem matters for your framework.
Specs comparison
| Spec | NVIDIA RTX 4090 | Apple M2 Ultra (192GB) |
|---|---|---|
| VRAM | 24 GB | 192 GB unified |
| Memory type | GDDR6X | LPDDR5 |
| Bandwidth | 1008 GB/s(+26%) | 800 GB/s |
| CPU cores | — | 24 (16P + 8E) |
| Architecture | Ada Lovelace | Apple M2 Ultra |
| Backend | CUDA | METAL |
| Tier | Consumer | Workstation |
| Released | 2022 | 2023 |
| Models (native) | 46 | 71 |
Estimated tokens per second
Computed from memory bandwidth and model active-parameter weight. Assumes model fits natively in VRAM.
| Model | NVIDIA RTX 4090 | Apple M2 Ultra (192GB) | Delta |
|---|---|---|---|
| Llama 3.3 70B Instruct(70B) | — | 4.5 t/s(BF16) | — |
| Qwen 3.6 27B(27B) | 46.7 t/s(NVFP4) | 5.9 t/s(FP32) | +692% |
| Llama 3.1 8B Instruct(8B) | 38.4 t/s(BF16) | 19.4 t/s(FP32) | +98% |
| Qwen 2.5 7B Instruct(7.6B) | 41.8 t/s(BF16) | 20.7 t/s(FP32) | +102% |
Delta is NVIDIA RTX 4090 relative to Apple M2 Ultra (192GB).
Only NVIDIA RTX 4090 can run(0)
No exclusive models — Apple M2 Ultra (192GB) can run everything NVIDIA RTX 4090 can.
Only Apple M2 Ultra (192GB) can run(25)
- MiniMax M3428B
- Llama 3.1 405B Instruct405B
- Llama 4 Maverick 400B400B
- GLM-4.7 358B358B
- GLM-4.5 355B355B
- GLM-4.6 355B355B
- DeepSeek V4 Flash 284B284B
- Qwen3 235B-A22B (MoE)235B
- MiniMax M2.5 229B229B
- MiniMax M2.7 229B229B
- Step 3.7 Flash198B
- Step 3.5 Flash196.81B
- +13 more
Both run natively(46)
These models fit in VRAM on both GPUs. Bandwidth determines which runs them faster.
- Mixtral 8x7B Instruct v0.137.5 t/svs7.4 t/s
- Qwen 3.5 35B-A3B (MoE)126.8 t/svs15.9 t/s
- Qwen 3.6 35B33.3 t/svs4.5 t/s
- Yi 1.5 34B Chat34.1 t/svs4.6 t/s
- Qwen3 32B36.9 t/svs4.8 t/s
- Qwen 2.5 32B Instruct35.6 t/svs4.8 t/s
- Qwen 2.5 Coder 32B Instruct35.6 t/svs4.8 t/s
- DeepSeek R1 Distill Qwen 32B35.6 t/svs4.8 t/s
- Nemotron 3 Nano 30B120.5 t/svs15.8 t/s
- Gemma 4 31B35 t/svs5 t/s
- Qwen3 30B-A3B (MoE)112.9 t/svs15.7 t/s
- Gemma 2 27B Instruct39.3 t/svs5.7 t/s
- Gemma 3 27B Instruct43.6 t/svs5.8 t/s
- Qwen 3.6 27B46.7 t/svs5.9 t/s
- Bonsai 27B74.4 t/svs72.6 t/s
- Gemma 4 26B (MoE)97.6 t/svs12.5 t/s
- +30 more on both
Which should you choose?
Choose NVIDIA RTX 4090 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 (>24 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 4090 or Apple M2 Ultra (192GB)?
- For local AI inference, the Apple M2 Ultra (192GB) has the edge. It offers 192 GB VRAM (vs 24 GB) and 800 GB/s bandwidth (vs 1008 GB/s), letting it run 71 models natively in VRAM vs 46 for its rival.
- How much VRAM does the NVIDIA RTX 4090 have vs the Apple M2 Ultra (192GB)?
- The NVIDIA RTX 4090 has 24 GB of GDDR6X at 1008 GB/s. The Apple M2 Ultra (192GB) has 192 GB of LPDDR5 at 800 GB/s. The Apple M2 Ultra (192GB) has 168 GB more VRAM, allowing it to run 25 models the NVIDIA RTX 4090 cannot fit natively.
- Can the NVIDIA RTX 4090 run Llama 3.3 70B?
- The NVIDIA RTX 4090 can run Llama 3.3 70B with CPU offload at NVFP4, but at reduced speed.
- 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 4090 and Apple M2 Ultra (192GB) for AI?
- The key difference for AI inference is VRAM and memory bandwidth. The NVIDIA RTX 4090 has 24 GB VRAM at 1008 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 4090 runs 46 models natively vs 71 for the Apple M2 Ultra (192GB).