Apple M5 Max (64GB)
The Apple M5 Max (64GB) has 64 GB VRAM and 614 GB/s memory bandwidth. It can run 54 of our 70 tracked models natively in VRAM at 8k context.
With 64 GB LPDDR5X, the Apple M5 Max (64GB) is a laptop-tier GPU that can run 54 models natively. It handles 70B-class models at Q4 quantization.
The Apple M5 Max (64GB) delivers serious local LLM inference capability, pairing 64GB of unified memory with 614 GB/s bandwidth for fast on-device AI on Mac. Qwen 3.6 35B fits at Q8_0 with headroom, and Gemma 4 31B runs at Q8_0 or F16 precision without CPU offload — covering today's most capable open-weight models at high quality. The 18-core CPU and tight integration with MLX and llama.cpp make the M5 Max (64GB) the go-to Apple Silicon choice for uncompromised local inference.
Apple M5 Max (64GB) is a mobile/laptop Apple Silicon chip based on the Apple M5 Max architecture. Released in 2026. It features 64 GB of LPDDR5X unified memory at 614 GB/s memory bandwidth. As an Apple Silicon chip, its memory is unified between CPU and GPU, so the full 64 GB can be allocated to model weights. MLX gives the best performance on Apple Silicon; llama.cpp Metal backend is a solid alternative. Both are well-supported by Ollama.
For local LLM inference, this GPU runs 54 of the 70 models we track natively in VRAM at 8K context. The largest model it handles in VRAM is Mixtral 8x22B Instruct v0.1 (52.6 t/s at Q2_K). It handles most models up to the 70B class in VRAM, including some larger MoE models. On Llama 3.3 70B Instruct, it achieves approximately 13.6 tokens per second at Q5_K_M quantization.
Apple's Metal backend is fully supported by MLX and llama.cpp, giving excellent performance on macOS. Among laptop GPUs, it sits above Apple M4 Max (64GB) and Apple M5 Max (48GB) in performance, but below Apple M1 Ultra (64GB).
| Vendor | Apple |
| Architecture | Apple M5 Max |
| CPU cores | 18 (6S + 12P) |
| VRAM | 64 GB (unified) |
| Memory type | LPDDR5X |
| Memory bandwidth | 614 GB/s |
| Compute backend | METAL |
| Tier | Laptop |
| Released | 2026 |
| Models (native) | 54 / 70 |
| Models (offload) | 0 / 70 |
Popular models for this GPU
Models this GPU runs natively in VRAM (54)
- Mixtral 8x22B Instruct v0.1141B · MMLU-Pro 40.0Q2_K · ~52.6 t/s
- Qwen 3.5 122B-A10B (MoE)122B · MMLU-Pro 86.7Q2_K · ~205.3 t/s
- Nemotron 3 Super 120B120B · MMLU-Pro 83.7Q3_K_M · ~130.9 t/s
- GPT-OSS 120B117B · MMLU-Pro 80.7Q3_K_M · ~314.1 t/s
- Llama 4 Scout 109B109B · MMLU-Pro 74.3Q3_K_M · ~92.4 t/s
- GLM-4.5 Air 106B106B · MMLU-Pro 81.4Q3_K_M · ~130.9 t/s
- GLM-4.6V 106B106B · MMLU-Pro 79.9Q3_K_M · ~130.9 t/s
- Qwen 2.5 72B Instruct72B · MMLU-Pro 71.1Q5_K_M · ~13.2 t/s
- Llama 3.3 70B Instruct70B · MMLU-Pro 68.9Q5_K_M · ~13.6 t/s
- DeepSeek R1 Distill Llama 70B70B · MMLU-Pro 70.0Q5_K_M · ~13.6 t/s
- Llama 3.1 70B Instruct70B · MMLU-Pro 66.4Q5_K_M · ~13.6 t/s
- Mixtral 8x7B Instruct v0.146.7B · MMLU-Pro 29.7Q8_0 · ~52.4 t/s
- Command-R 35B35B · MMLU-Pro 33.0Q8_0 · ~17.5 t/s
- Qwen 3.5 35B-A3B (MoE)35B · MMLU-Pro 84.2Q8_0 · ~225.1 t/s
- Qwen 3.6 35B35B · MMLU-Pro 85.2Q8_0 · ~17.5 t/s
- Yi 1.5 34B Chat34.4B · MMLU-Pro 37.0Q8_0 · ~17.8 t/s
- Qwen3 32B32.8B · MMLU-Pro 65.5Q8_0 · ~18.7 t/s
- Qwen 2.5 32B Instruct32.5B · MMLU-Pro 69.0Q8_0 · ~18.9 t/s
- Qwen 2.5 Coder 32B Instruct32.5B · MMLU-Pro 50.4Q8_0 · ~18.9 t/s
- DeepSeek R1 Distill Qwen 32B32.5B · MMLU-Pro 65.0Q8_0 · ~18.9 t/s
- Nemotron 3 Nano 30B32B · MMLU-Pro 78.3Q8_0 · ~225.1 t/s
- Gemma 4 31B31B · MMLU-Pro 85.2Q8_0 · ~19.8 t/s
- Qwen3 30B-A3B (MoE)30B · MMLU-Pro 61.5Q8_0 · ~225.1 t/s
- Gemma 2 27B Instruct27.2B · MMLU-Pro 38.0Q8_0 · ~22.6 t/s
- Gemma 3 27B Instruct27B · MMLU-Pro 67.5Q8_0 · ~22.7 t/s
- Qwen 3.6 27B27B · MMLU-Pro 86.2Q8_0 · ~22.7 t/s
- Gemma 4 26B (MoE)26B · MMLU-Pro 82.6BF16 · ~88.9 t/s
- Mistral Small 3.1 24B Instruct24B · MMLU-Pro 66.8BF16 · ~12.8 t/s
- Mistral Small 22B22.2B · MMLU-Pro 49.2BF16 · ~13.8 t/s
- GPT-OSS 20B21B · MMLU-Pro 67.9BF16 · ~84.4 t/s
- Qwen3 14B14.8B · MMLU-Pro 61.0BF16 · ~20.7 t/s
- Qwen 2.5 14B Instruct14.7B · MMLU-Pro 63.7BF16 · ~20.9 t/s
- Phi-4 14B Instruct14B · MMLU-Pro 70.4BF16 · ~21.9 t/s
- Mistral Nemo 12B Instruct12.2B · MMLU-Pro 35.6FP32 · ~12.6 t/s
- Gemma 3 12B Instruct12.2B · MMLU-Pro 60.6FP32 · ~12.6 t/s
- Gemma 2 9B Instruct9.2B · MMLU-Pro 32.0FP32 · ~16.7 t/s
- Llama 3.1 8B Instruct8B · MMLU-Pro 48.3FP32 · ~19.2 t/s
- DeepSeek R1 Distill Llama 8B8B · MMLU-Pro 41.0FP32 · ~19.2 t/s
- Qwen3 8B8B · MMLU-Pro 56.7FP32 · ~19.2 t/s
- Qwen 2.5 7B Instruct7.6B · MMLU-Pro 56.3FP32 · ~20.2 t/s
- Mistral 7B Instruct v0.37.25B · MMLU-Pro 30.0FP32 · ~21.2 t/s
- Gemma 3 4B Instruct4B · MMLU-Pro 43.6FP32 · ~38.4 t/s
- Gemma 4 E4B4B · MMLU-Pro 69.4FP32 · ~38.4 t/s
- Phi-3.5 Mini Instruct3.8B · MMLU-Pro 47.4FP32 · ~40.4 t/s
- Llama 3.2 3B Instruct3.2B · MMLU-Pro 24.0FP32 · ~48 t/s
- Qwen 2.5 3B Instruct3.1B · MMLU-Pro 32.4FP32 · ~49.5 t/s
- Gemma 2 2B Instruct2.6B · MMLU-Pro 17.8FP32 · ~59 t/s
- Gemma 4 E2B2B · MMLU-Pro 60.0FP32 · ~76.8 t/s
- SmolLM2 1.7B Instruct1.7B · MMLU-Pro 19.0FP32 · ~90.3 t/s
- Qwen 2.5 1.5B Instruct1.5B · MMLU-Pro 16.8FP32 · ~102.3 t/s
- Llama 3.2 1B Instruct1.24B · MMLU-Pro 12.5FP32 · ~123.8 t/s
- Gemma 3 1B Instruct1B · MMLU-Pro 14.7FP32 · ~153.5 t/s
- Qwen 2.5 0.5B Instruct0.5B · MMLU-Pro 10.0FP32 · ~307 t/s
- SmolLM2 360M Instruct0.36B · MMLU-Pro 8.0FP32 · ~426.4 t/s
Too large for this GPU (16)
Frequently asked questions
- How much VRAM does the Apple M5 Max (64GB) have?
- The Apple M5 Max (64GB) has 64 GB of LPDDR5X with 614 GB/s memory bandwidth (unified system memory, shared between CPU and GPU).
- What is the Apple M5 Max (64GB) best for?
- With 64 GB of VRAM, the Apple M5 Max (64GB) 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 M5 Max (64GB) run locally?
- The Apple M5 Max (64GB) can run 54 of the 70 open-weight models tracked by CanItRun natively in VRAM at 8k context. Top options include: Llama 3.3 70B Instruct at Q5_K_M, Llama 3.1 8B Instruct at FP32, Llama 3.2 3B Instruct at FP32.
- Can the Apple M5 Max (64GB) run Llama 3.3 70B Instruct?
- Yes. The Apple M5 Max (64GB) runs Llama 3.3 70B Instruct natively in VRAM at Q5_K_M quantization, achieving approximately 13.6 tokens per second.
- Can the Apple M5 Max (64GB) run Qwen 3.6 27B?
- Yes. The Apple M5 Max (64GB) runs Qwen 3.6 27B natively in VRAM at Q8_0 quantization, achieving approximately 22.7 tokens per second.
- Can the Apple M5 Max (64GB) run Llama 3.1 8B Instruct?
- Yes. The Apple M5 Max (64GB) runs Llama 3.1 8B Instruct natively in VRAM at FP32 quantization, achieving approximately 19.2 tokens per second.