Apple M4 Pro (48GB)
The Apple M4 Pro (48GB) has 48 GB VRAM and 273 GB/s memory bandwidth. It can run 51 of our 70 tracked models natively in VRAM at 8k context.
With 48 GB LPDDR5X, the Apple M4 Pro (48GB) is a laptop-tier GPU that can run 51 models natively. It handles 70B-class models at Q4 quantization.
Apple M4 Pro (48GB): 48GB LPDDR5X at 273 GB/s. 14-core CPU (10P+4E). 36MB L2 cache.
32B at Q4 ~10-15 t/s. 70B at Q2-Q3 ~7-12 t/s decode. Studied in academic benchmarks.
Full llama.cpp K-quants support. MLX optimized. $0.143/hour amortized cost vs $0.384/hour RTX A6000.
| Vendor | Apple |
| Architecture | Apple M4 Pro |
| CPU cores | 14 (10P + 4E) |
| VRAM | 48 GB (unified) |
| Memory type | LPDDR5X |
| Memory bandwidth | 273 GB/s |
| Compute backend | METAL |
| Tier | Laptop |
| Released | 2024 |
| Models (native) | 51 / 70 |
| Models (offload) | 0 / 70 |
Software: MLX gives the best performance on Apple Silicon; llama.cpp Metal backend is a solid alternative. Both are well-supported by Ollama.
Popular models for this GPU
Models this GPU runs natively in VRAM (51)
- GPT-OSS 120B117B · MMLU-Pro 80.7Q2_K · ~182.6 t/s
- Llama 4 Scout 109B109B · MMLU-Pro 74.3Q2_K · ~53.7 t/s
- GLM-4.5 Air 106B106B · MMLU-Pro 81.4Q2_K · ~76.1 t/s
- GLM-4.6V 106B106B · MMLU-Pro 79.9Q2_K · ~76.1 t/s
- Qwen 2.5 72B Instruct72B · MMLU-Pro 71.1Q3_K_M · ~8.8 t/s
- Llama 3.3 70B Instruct70B · MMLU-Pro 68.9Q3_K_M · ~9.1 t/s
- DeepSeek R1 Distill Llama 70B70B · MMLU-Pro 70.0Q3_K_M · ~9.1 t/s
- Llama 3.1 70B Instruct70B · MMLU-Pro 66.4Q3_K_M · ~9.1 t/s
- Mixtral 8x7B Instruct v0.146.7B · MMLU-Pro 29.7Q5_K_M · ~36.1 t/s
- Command-R 35B35B · MMLU-Pro 33.0Q5_K_M · ~12.1 t/s
- Qwen 3.5 35B-A3B (MoE)35B · MMLU-Pro 84.2Q8_0 · ~100.1 t/s
- Qwen 3.6 35B35B · MMLU-Pro 85.2Q8_0 · ~7.8 t/s
- Yi 1.5 34B Chat34.4B · MMLU-Pro 37.0Q8_0 · ~7.9 t/s
- Qwen3 32B32.8B · MMLU-Pro 65.5Q8_0 · ~8.3 t/s
- Qwen 2.5 32B Instruct32.5B · MMLU-Pro 69.0Q8_0 · ~8.4 t/s
- Qwen 2.5 Coder 32B Instruct32.5B · MMLU-Pro 50.4Q8_0 · ~8.4 t/s
- DeepSeek R1 Distill Qwen 32B32.5B · MMLU-Pro 65.0Q8_0 · ~8.4 t/s
- Nemotron 3 Nano 30B32B · MMLU-Pro 78.3Q8_0 · ~100.1 t/s
- Gemma 4 31B31B · MMLU-Pro 85.2Q8_0 · ~8.8 t/s
- Qwen3 30B-A3B (MoE)30B · MMLU-Pro 61.5Q8_0 · ~100.1 t/s
- Gemma 2 27B Instruct27.2B · MMLU-Pro 38.0Q8_0 · ~10 t/s
- Gemma 3 27B Instruct27B · MMLU-Pro 67.5Q8_0 · ~10.1 t/s
- Qwen 3.6 27B27B · MMLU-Pro 86.2Q8_0 · ~10.1 t/s
- Gemma 4 26B (MoE)26B · MMLU-Pro 82.6Q8_0 · ~79 t/s
- Mistral Small 3.1 24B Instruct24B · MMLU-Pro 66.8Q8_0 · ~11.4 t/s
- Mistral Small 22B22.2B · MMLU-Pro 49.2Q8_0 · ~12.3 t/s
- GPT-OSS 20B21B · MMLU-Pro 67.9Q8_0 · ~75.1 t/s
- Qwen3 14B14.8B · MMLU-Pro 61.0BF16 · ~9.2 t/s
- Qwen 2.5 14B Instruct14.7B · MMLU-Pro 63.7BF16 · ~9.3 t/s
- Phi-4 14B Instruct14B · MMLU-Pro 70.4BF16 · ~9.8 t/s
- Mistral Nemo 12B Instruct12.2B · MMLU-Pro 35.6BF16 · ~11.2 t/s
- Gemma 3 12B Instruct12.2B · MMLU-Pro 60.6BF16 · ~11.2 t/s
- Gemma 2 9B Instruct9.2B · MMLU-Pro 32.0BF16 · ~14.8 t/s
- Llama 3.1 8B Instruct8B · MMLU-Pro 48.3FP32 · ~8.5 t/s
- DeepSeek R1 Distill Llama 8B8B · MMLU-Pro 41.0FP32 · ~8.5 t/s
- Qwen3 8B8B · MMLU-Pro 56.7FP32 · ~8.5 t/s
- Qwen 2.5 7B Instruct7.6B · MMLU-Pro 56.3FP32 · ~9 t/s
- Mistral 7B Instruct v0.37.25B · MMLU-Pro 30.0FP32 · ~9.4 t/s
- Gemma 3 4B Instruct4B · MMLU-Pro 43.6FP32 · ~17.1 t/s
- Gemma 4 E4B4B · MMLU-Pro 69.4FP32 · ~17.1 t/s
- Phi-3.5 Mini Instruct3.8B · MMLU-Pro 47.4FP32 · ~18 t/s
- Llama 3.2 3B Instruct3.2B · MMLU-Pro 24.0FP32 · ~21.3 t/s
- Qwen 2.5 3B Instruct3.1B · MMLU-Pro 32.4FP32 · ~22 t/s
- Gemma 2 2B Instruct2.6B · MMLU-Pro 17.8FP32 · ~26.3 t/s
- Gemma 4 E2B2B · MMLU-Pro 60.0FP32 · ~34.1 t/s
- SmolLM2 1.7B Instruct1.7B · MMLU-Pro 19.0FP32 · ~40.1 t/s
- Qwen 2.5 1.5B Instruct1.5B · MMLU-Pro 16.8FP32 · ~45.5 t/s
- Llama 3.2 1B Instruct1.24B · MMLU-Pro 12.5FP32 · ~55 t/s
- Gemma 3 1B Instruct1B · MMLU-Pro 14.7FP32 · ~68.3 t/s
- Qwen 2.5 0.5B Instruct0.5B · MMLU-Pro 10.0FP32 · ~136.5 t/s
- SmolLM2 360M Instruct0.36B · MMLU-Pro 8.0FP32 · ~189.6 t/s
Too large for this GPU (19)
- Mixtral 8x22B Instruct v0.1
- Llama 3.1 405B Instruct
- DeepSeek V3 671B
- DeepSeek R1 671B
- Llama 4 Maverick 400B
- Qwen3 235B-A22B (MoE)
- MiniMax M1 456B
- GLM-4.5 355B
- GLM-4.6 355B
- GLM-4.7 358B
- Qwen 3.5 122B-A10B (MoE)
- MiniMax M2.5 229B
- GLM-5 744B
- MiniMax M2.7 229B
- Nemotron 3 Super 120B
- Kimi K2.6
- GLM-5.1 754B
- DeepSeek V4 Pro 1.6T
- DeepSeek V4 Flash 284B
Compare Apple M4 Pro (48GB) with other GPUs
Frequently asked questions
- How much VRAM does the Apple M4 Pro (48GB) have?
- The Apple M4 Pro (48GB) has 48 GB of LPDDR5X with 273 GB/s memory bandwidth (unified system memory, shared between CPU and GPU).
- What is the Apple M4 Pro (48GB) best for?
- With 48 GB of VRAM, the Apple M4 Pro (48GB) is ideal for running 70B-class models at Q4 quantization and large MoE models — a workstation sweet spot for local inference.
- What LLMs can the Apple M4 Pro (48GB) run locally?
- The Apple M4 Pro (48GB) can run 51 of the 70 open-weight models tracked by CanItRun natively in VRAM at 8k context. Top options include: Llama 3.3 70B Instruct at Q3_K_M, Llama 3.1 8B Instruct at FP32, Llama 3.2 3B Instruct at FP32.
- Can the Apple M4 Pro (48GB) run Llama 3.3 70B Instruct?
- Yes. The Apple M4 Pro (48GB) runs Llama 3.3 70B Instruct natively in VRAM at Q3_K_M quantization, achieving approximately 9.1 tokens per second.
- Can the Apple M4 Pro (48GB) run Qwen 3.6 27B?
- Yes. The Apple M4 Pro (48GB) runs Qwen 3.6 27B natively in VRAM at Q8_0 quantization, achieving approximately 10.1 tokens per second.
- Can the Apple M4 Pro (48GB) run Llama 3.1 8B Instruct?
- Yes. The Apple M4 Pro (48GB) runs Llama 3.1 8B Instruct natively in VRAM at FP32 quantization, achieving approximately 8.5 tokens per second.