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 110 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 50 models natively (vs 49), and exclusively fits 1 model 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) | 49 | 50 |
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) | 39.6 t/s(Q3_K_M) | 16.1 t/s(Q3_K_M) | +146% |
| Llama 3.1 8B Instruct(8B) | 58.5 t/s(Q8_0) | 22.8 t/s(Q8_0) | +157% |
| Qwen 2.5 7B Instruct(7.6B) | 65.7 t/s(Q8_0) | 25.5 t/s(Q8_0) | +158% |
Delta is NVIDIA RTX 4080 relative to Apple M4 Pro (24GB), compared at the highest quantization that fits natively on both cards.
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(49)
These models fit in VRAM on both GPUs. Bandwidth determines which runs them faster.
- Qwen 3.5 35B-A3B (MoE)186.7 t/svs67.6 t/s
- Qwen 3.6 35B-A3B (MoE)186.7 t/svs67.6 t/s
- Ornith 1.5 35B-A3B (MoE)186.7 t/svs67.6 t/s
- Gemma 4 31B40.8 t/svs16.5 t/s
- Qwen3 30B-A3B (MoE)142.4 t/svs51.3 t/s
- Muse Glimmer 30B40.6 t/svs16.1 t/s
- Gemma 2 27B Instruct41.5 t/svs16.2 t/s
- Gemma 3 27B Instruct46.5 t/svs18.5 t/s
- Qwen 3.6 27B39.6 t/svs16.1 t/s
- UI-Mate 27B39.6 t/svs16.1 t/s
- Bonsai 27B62.1 t/svs28.2 t/s
- Bonsai 2 27B71.2 t/svs33.9 t/s
- Qwen 3.8 27B39.6 t/svs16.1 t/s
- Gemma 4 26B (MoE)167.9 t/svs59.9 t/s
- Mistral Small 3.1 24B Instruct43.8 t/svs16.9 t/s
- Mistral Small 22B43 t/svs17.4 t/s
- +33 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 (>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 50 models natively in VRAM vs 49 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 model 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 49 models natively vs 50 for the Apple M4 Pro (24GB).