NVIDIA RTX 5090 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
Apple M3 Ultra (96GB) wins for local AI inference. It has 64 GB more VRAM and -54% more memory bandwidth, runs 61 models natively (vs 47), and exclusively fits 14 models the other cannot. Note: NVIDIA RTX 5090 uses CUDA while Apple M3 Ultra (96GB) uses METAL — software ecosystem matters for your framework.
Specs comparison
| Spec | NVIDIA RTX 5090 | Apple M3 Ultra (96GB) |
|---|---|---|
| VRAM | 32 GB | 96 GB unified |
| Memory type | GDDR7 | LPDDR5X |
| Bandwidth | 1792 GB/s(+119%) | 819 GB/s |
| CPU cores | — | 28 (20P + 8E) |
| Architecture | Blackwell | Apple M3 Ultra |
| Backend | CUDA | METAL |
| Tier | Consumer | Workstation |
| Released | 2025 | 2025 |
| Models (native) | 47 | 61 |
Estimated tokens per second
Computed from memory bandwidth and model active-parameter weight. Assumes model fits natively in VRAM.
| Model | NVIDIA RTX 5090 | Apple M3 Ultra (96GB) | Delta |
|---|---|---|---|
| Llama 3.3 70B Instruct(70B) | — | 8.5 t/s(Q8_0) | — |
| Qwen 3.6 27B(27B) | 83 t/s(NVFP4) | 12 t/s(BF16) | +592% |
| Llama 3.1 8B Instruct(8B) | 68.2 t/s(BF16) | 19.8 t/s(FP32) | +244% |
| Qwen 2.5 7B Instruct(7.6B) | 74.3 t/s(BF16) | 21.2 t/s(FP32) | +250% |
Delta is NVIDIA RTX 5090 relative to Apple M3 Ultra (96GB).
Only NVIDIA RTX 5090 can run(0)
No exclusive models — Apple M3 Ultra (96GB) can run everything NVIDIA RTX 5090 can.
Only Apple M3 Ultra (96GB) can run(14)
Both run natively(47)
These models fit in VRAM on both GPUs. Bandwidth determines which runs them faster.
- Mixtral 8x7B Instruct v0.151.6 t/svs14 t/s
- Command-R 35B48.4 t/svs13.7 t/s
- Qwen 3.5 35B-A3B (MoE)225.4 t/svs32.5 t/s
- Qwen 3.6 35B59.3 t/svs9.1 t/s
- Yi 1.5 34B Chat60.6 t/svs9.3 t/s
- Qwen3 32B65.7 t/svs9.8 t/s
- Qwen 2.5 32B Instruct63.3 t/svs9.8 t/s
- Qwen 2.5 Coder 32B Instruct63.3 t/svs9.8 t/s
- DeepSeek R1 Distill Qwen 32B63.3 t/svs9.8 t/s
- Nemotron 3 Nano 30B214.3 t/svs32.1 t/s
- Gemma 4 31B62.2 t/svs10 t/s
- Qwen3 30B-A3B (MoE)200.6 t/svs31.5 t/s
- Gemma 2 27B Instruct69.8 t/svs11.4 t/s
- Gemma 3 27B Instruct77.4 t/svs11.8 t/s
- Qwen 3.6 27B83 t/svs12 t/s
- Bonsai 27B132.2 t/svs74.4 t/s
- +31 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 (>32 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 5090 or Apple M3 Ultra (96GB)?
- For local AI inference, the Apple M3 Ultra (96GB) has the edge. It offers 96 GB VRAM (vs 32 GB) and 819 GB/s bandwidth (vs 1792 GB/s), letting it run 61 models natively in VRAM vs 47 for its rival.
- How much VRAM does the NVIDIA RTX 5090 have vs the Apple M3 Ultra (96GB)?
- The NVIDIA RTX 5090 has 32 GB of GDDR7 at 1792 GB/s. The Apple M3 Ultra (96GB) has 96 GB of LPDDR5X at 819 GB/s. The Apple M3 Ultra (96GB) has 64 GB more VRAM, allowing it to run 14 models the NVIDIA RTX 5090 cannot fit natively.
- Can the NVIDIA RTX 5090 run Llama 3.3 70B?
- The NVIDIA RTX 5090 can run Llama 3.3 70B with CPU offload at NVFP4, but at reduced speed.
- 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 5090 and Apple M3 Ultra (96GB) for AI?
- The key difference for AI inference is VRAM and memory bandwidth. The NVIDIA RTX 5090 has 32 GB VRAM at 1792 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 5090 runs 47 models natively vs 61 for the Apple M3 Ultra (96GB).