NVIDIA RTX 4080 vs Apple M4 Pro (24GB)
Side-by-side local AI comparison: VRAM, memory bandwidth, model compatibility, and estimated tokens per second across 99 open-weight models.
Quick verdict
Apple M4 Pro (24GB) wins for local AI inference. It has 8 GB more VRAM and -61.9% more memory bandwidth, runs 47 models natively (vs 46), and exclusively fits 1 models the other cannot. Note: NVIDIA RTX 4080 uses CUDA while Apple M4 Pro (24GB) uses METAL; software ecosystem matters for your framework.
Specs comparison
| Spec | NVIDIA RTX 4080 | Apple M4 Pro (24GB) |
|---|---|---|
| VRAM | 16 GB | 24 GB unified |
| Memory type | GDDR6X | LPDDR5X |
| Bandwidth | 717 GB/s(+163%) | 273 GB/s |
| CPU cores | N/A | 12 (8P + 4E) |
| Architecture | Ada Lovelace | Apple M4 Pro |
| Backend | CUDA | METAL |
| Tier | Consumer | Laptop |
| Released | 2022 | 2024 |
| Models (native) | 46 | 47 |
Estimated tokens per second
Computed from memory bandwidth and model active-parameter weight. Assumes model fits natively in VRAM.
| Model | NVIDIA RTX 4080 | Apple M4 Pro (24GB) | Delta |
|---|---|---|---|
| Llama 3.3 70B Instruct(70B) | N/A | N/A | N/A |
| Qwen 3.6 27B(27B) | 34.5 t/s(Q3_K_M) | 16.1 t/s(Q3_K_M) | +114% |
| Llama 3.1 8B Instruct(8B) | 48.7 t/s(Q8_0) | 22.8 t/s(Q8_0) | +114% |
| Qwen 2.5 7B Instruct(7.6B) | 54.5 t/s(Q8_0) | 25.5 t/s(Q8_0) | +114% |
Delta is NVIDIA RTX 4080 relative to Apple M4 Pro (24GB).
Only NVIDIA RTX 4080 can run(0)
No exclusive models: Apple M4 Pro (24GB) can run everything NVIDIA RTX 4080 can.
Only Apple M4 Pro (24GB) can run(1)
- Qwen3 32B32.8B
Both run natively(46)
These models fit in VRAM on both GPUs. Bandwidth determines which runs them faster.
- Qwen 3.5 35B-A3B (MoE)117.2 t/svs54.9 t/s
- Ornith 1.5 35B-A3B (MoE)117.2 t/svs54.9 t/s
- Nemotron 3 Nano 30B109.8 t/svs51.4 t/s
- Gemma 4 31B35.3 t/svs16.5 t/s
- Qwen3 30B-A3B (MoE)101 t/svs47.3 t/s
- Nemotron 3.5 Lightning 30B-A3B120.7 t/svs56.6 t/s
- Muse Glimmer 30B34.4 t/svs16.1 t/s
- Gemma 2 27B Instruct34.7 t/svs16.2 t/s
- Gemma 3 27B Instruct39.4 t/svs18.5 t/s
- Qwen 3.6 27B34.5 t/svs16.1 t/s
- UI-Mate 27B34.5 t/svs16.1 t/s
- Bonsai 27B60.2 t/svs28.2 t/s
- Bonsai 2 27B72.4 t/svs33.9 t/s
- Qwen 3.8 27B34.5 t/svs16.1 t/s
- Gemma 4 26B (MoE)72 t/svs33.8 t/s
- Mistral Small 3.1 24B Instruct36.2 t/svs16.9 t/s
- +30 more on both
Which should you choose?
Choose NVIDIA RTX 4080 if:
- • Faster token generation is the priority
- • You rely on CUDA-based tools (PyTorch, vLLM, Ollama)
Choose Apple M4 Pro (24GB) if:
- • You need to run larger models (>16 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 4080 or Apple M4 Pro (24GB)?
- For local AI inference, the Apple M4 Pro (24GB) has the edge. It offers 24 GB VRAM (vs 16 GB) and 273 GB/s bandwidth (vs 717 GB/s), letting it run 47 models natively in VRAM vs 46 for its rival.
- How much VRAM does the NVIDIA RTX 4080 have vs the Apple M4 Pro (24GB)?
- The NVIDIA RTX 4080 has 16 GB of GDDR6X at 717 GB/s. The Apple M4 Pro (24GB) has 24 GB of LPDDR5X at 273 GB/s. The Apple M4 Pro (24GB) has 8 GB more VRAM, allowing it to run 1 models the NVIDIA RTX 4080 cannot fit natively.
- Can the NVIDIA RTX 4080 run Llama 3.3 70B?
- The NVIDIA RTX 4080 can run Llama 3.3 70B with CPU offload at Q3_K_M, but at reduced speed.
- Can the Apple M4 Pro (24GB) run Llama 3.3 70B?
- The Apple M4 Pro (24GB) does not have enough VRAM to run Llama 3.3 70B.
- What is the difference between the NVIDIA RTX 4080 and Apple M4 Pro (24GB) for AI?
- The key difference for AI inference is VRAM and memory bandwidth. The NVIDIA RTX 4080 has 16 GB VRAM at 717 GB/s (CUDA backend). The Apple M4 Pro (24GB) has 24 GB VRAM at 273 GB/s (METAL backend). VRAM determines which models fit; bandwidth determines tokens per second. The NVIDIA RTX 4080 runs 46 models natively vs 47 for the Apple M4 Pro (24GB).