NVIDIA RTX 5090 vs Apple M4 Max (128GB)
Side-by-side local AI comparison: VRAM, memory bandwidth, model compatibility, and estimated tokens per second across 110 open-weight models.
Quick verdict
Apple M4 Max (128GB) wins for local AI inference. It has 96 GB more VRAM and -69.5% more memory bandwidth, runs 81 models natively (vs 60), and exclusively fits 21 models the other cannot. Note: NVIDIA RTX 5090 uses CUDA while Apple M4 Max (128GB) uses METAL; software ecosystem matters for your framework.
Specs comparison
| Spec | NVIDIA RTX 5090 | Apple M4 Max (128GB) |
|---|---|---|
| VRAM | 32 GB | 128 GB unified |
| Memory type | GDDR7 | LPDDR5X |
| Bandwidth | 1792 GB/s(+228%) | 546 GB/s |
| CPU cores | N/A | 16 (12P + 4E) |
| Architecture | Blackwell | Apple M4 Max |
| Backend | CUDA | METAL |
| Tier | Consumer | Laptop |
| Released | 2025 | 2024 |
| Models (native) | 60 | 81 |
Estimated tokens per second
Computed from memory bandwidth and model active-parameter weight. Assumes model fits natively in VRAM.
| Model | NVIDIA RTX 5090 | Apple M4 Max (128GB) | Delta |
|---|---|---|---|
| Llama 3.3 70B Instruct(70B) | N/A | 5.7 t/s(Q8_0) | N/A |
| Qwen 3.6 27B(27B) | 78.5 t/s(NVFP4) | 8 t/s(BF16) | +185%at Q6_K |
| Llama 3.1 8B Instruct(8B) | 78.4 t/s(BF16) | 25.6 t/s(BF16) | +206% |
| Qwen 2.5 7B Instruct(7.6B) | 86.1 t/s(BF16) | 27.9 t/s(BF16) | +209% |
Delta is NVIDIA RTX 5090 relative to Apple M4 Max (128GB), compared at the highest quantization that fits natively on both cards.
Only NVIDIA RTX 5090 can run(0)
No exclusive models: Apple M4 Max (128GB) can run everything NVIDIA RTX 5090 can.
Only Apple M4 Max (128GB) can run(21)
- DeepSeek V4 Flash 0731 284B284B
- Qwen3 235B-A22B (MoE)235B
- MiniMax M2.5 229B229B
- MiniMax M2.7 229B229B
- Step 3.7 Flash198B
- Step 3.5 Flash196.81B
- Qwen3.8-Flash-Next180B
- Mixtral 8x22B Instruct v0.1141B
- Mistral Medium 3.5 128B128B
- Ornith 1.5 122B (MoE)124.6B
- Qwen 3.5 122B-A10B (MoE)122B
- Nemotron 3 Super 120B120B
- +9 more
Both run natively(60)
These models fit in VRAM on both GPUs. Bandwidth determines which runs them faster.
- Mixtral 8x7B Instruct v0.1135.2 t/svs14.6 t/s
- Flint 40B (MoE)196 t/svs43.8 t/s
- Command-R 35B56.6 t/svs5.4 t/s
- Qwen 3.5 35B-A3B (MoE)230.5 t/svs43.6 t/s
- Qwen 3.6 35B-A3B (MoE)230.5 t/svs43.6 t/s
- Ornith 1.5 35B-A3B (MoE)230.5 t/svs43.6 t/s
- Yi 1.5 34B Chat63.4 t/svs6.2 t/s
- Nanbeige4.3 33B54.8 t/svs6.4 t/s
- Qwen3 32B66.3 t/svs6.5 t/s
- Qwen 2.5 32B Instruct64.5 t/svs6.5 t/s
- Qwen 2.5 Coder 32B Instruct64.5 t/svs6.5 t/s
- DeepSeek R1 Distill Qwen 32B64.5 t/svs6.5 t/s
- Nemotron 3 Nano 30B183.6 t/svs39.5 t/s
- Gemma 4 31B70 t/svs6.9 t/s
- Qwen3 30B-A3B (MoE)185.3 t/svs38.3 t/s
- Nemotron 3.5 Lightning 30B-A3B176.5 t/svs37.6 t/s
- +44 more on both
Which should you choose?
Choose NVIDIA RTX 5090 if:
- • Faster token generation is the priority
- • You rely on CUDA-based tools (PyTorch, vLLM, Ollama)
- • You want the newer architecture and longer driver support lifecycle
Choose Apple M4 Max (128GB) if:
- • 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 M4 Max (128GB)?
- For local AI inference, the Apple M4 Max (128GB) has the edge. It offers 128 GB VRAM (vs 32 GB) and 546 GB/s bandwidth (vs 1792 GB/s), letting it run 81 models natively in VRAM vs 60 for its rival.
- How much VRAM does the NVIDIA RTX 5090 have vs the Apple M4 Max (128GB)?
- The NVIDIA RTX 5090 has 32 GB of GDDR7 at 1792 GB/s. The Apple M4 Max (128GB) has 128 GB of LPDDR5X at 546 GB/s. The Apple M4 Max (128GB) has 96 GB more VRAM, allowing it to run 21 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 M4 Max (128GB) run Llama 3.3 70B?
- Yes. The Apple M4 Max (128GB) runs Llama 3.3 70B natively at Q8_0 quantization at approximately 5.7 tokens per second.
- What is the difference between the NVIDIA RTX 5090 and Apple M4 Max (128GB) 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 M4 Max (128GB) has 128 GB VRAM at 546 GB/s (METAL backend). VRAM determines which models fit; bandwidth determines tokens per second. The NVIDIA RTX 5090 runs 60 models natively vs 81 for the Apple M4 Max (128GB).