NVIDIA RTX 4090 vs Apple M3 Max (128GB)
Side-by-side local AI comparison — VRAM, memory bandwidth, model compatibility, and estimated tokens per second across 70 open-weight models.
Quick verdict
Apple M3 Max (128GB) wins for local AI inference. It has 104 GB more VRAM and -60% more memory bandwidth, runs 58 models natively (vs 42), and exclusively fits 16 models the other cannot. Note: NVIDIA RTX 4090 uses CUDA while Apple M3 Max (128GB) uses METAL — software ecosystem matters for your framework.
Specs comparison
| Spec | NVIDIA RTX 4090 | Apple M3 Max (128GB) |
|---|---|---|
| VRAM | 24 GB | 128 GB unified |
| Memory type | GDDR6X | LPDDR5 |
| Bandwidth | 1008 GB/s(+152%) | 400 GB/s |
| CPU cores | — | 16 (12P + 4E) |
| Architecture | Ada Lovelace | Apple M3 Max |
| Backend | CUDA | METAL |
| Tier | Consumer | Laptop |
| Released | 2022 | 2023 |
| Models (native) | 42 | 58 |
Estimated tokens per second
Computed from memory bandwidth and model active-parameter weight. Assumes model fits natively in VRAM.
| Model | NVIDIA RTX 4090 | Apple M3 Max (128GB) | Delta |
|---|---|---|---|
| Llama 3.3 70B Instruct(70B) | — | 5.7 t/s(Q8_0) | — |
| Qwen 3.6 27B(27B) | 74.7 t/s(NVFP4) | 3.7 t/s(FP32) | +1919% |
| Llama 3.1 8B Instruct(8B) | 63 t/s(BF16) | 12.5 t/s(FP32) | +404% |
| Qwen 2.5 7B Instruct(7.6B) | 66.3 t/s(BF16) | 13.2 t/s(FP32) | +402% |
Delta is NVIDIA RTX 4090 relative to Apple M3 Max (128GB).
Only NVIDIA RTX 4090 can run(0)
No exclusive models — Apple M3 Max (128GB) can run everything NVIDIA RTX 4090 can.
Only Apple M3 Max (128GB) can run(16)
Both run natively(42)
These models fit in VRAM on both GPUs. Bandwidth determines which runs them faster.
- Mixtral 8x7B Instruct v0.1261.3 t/svs17.1 t/s
- Qwen 3.5 35B-A3B (MoE)739.2 t/svs73.3 t/s
- Qwen 3.6 35B57.6 t/svs5.7 t/s
- Yi 1.5 34B Chat58.6 t/svs5.8 t/s
- Qwen3 32B61.5 t/svs6.1 t/s
- Qwen 2.5 32B Instruct62 t/svs6.2 t/s
- Qwen 2.5 Coder 32B Instruct62 t/svs6.2 t/s
- DeepSeek R1 Distill Qwen 32B62 t/svs6.2 t/s
- Nemotron 3 Nano 30B739.2 t/svs73.3 t/s
- Gemma 4 31B65 t/svs6.5 t/s
- Qwen3 30B-A3B (MoE)739.2 t/svs73.3 t/s
- Gemma 2 27B Instruct74.1 t/svs7.4 t/s
- Gemma 3 27B Instruct74.7 t/svs3.7 t/s
- Qwen 3.6 27B74.7 t/svs3.7 t/s
- Gemma 4 26B (MoE)583.6 t/svs28.9 t/s
- Mistral Small 3.1 24B Instruct84 t/svs4.2 t/s
- +26 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 (>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 M3 Max (128GB)?
- For local AI inference, the Apple M3 Max (128GB) has the edge. It offers 128 GB VRAM (vs 24 GB) and 400 GB/s bandwidth (vs 1008 GB/s), letting it run 58 models natively in VRAM vs 42 for its rival.
- How much VRAM does the NVIDIA RTX 4090 have vs the Apple M3 Max (128GB)?
- The NVIDIA RTX 4090 has 24 GB of GDDR6X at 1008 GB/s. The Apple M3 Max (128GB) has 128 GB of LPDDR5 at 400 GB/s. The Apple M3 Max (128GB) has 104 GB more VRAM, allowing it to run 16 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 M3 Max (128GB) run Llama 3.3 70B?
- Yes. The Apple M3 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 4090 and Apple M3 Max (128GB) 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 M3 Max (128GB) has 128 GB VRAM at 400 GB/s (METAL backend). VRAM determines which models fit; bandwidth determines tokens per second. The NVIDIA RTX 4090 runs 42 models natively vs 58 for the Apple M3 Max (128GB).