Apple M5 Pro (36GB)
The Apple M5 Pro (36GB) has 36 GB VRAM and 307 GB/s memory bandwidth. It can run 45 of our 81 tracked models natively in VRAM at 8k context.
With 36 GB LPDDR5X, the Apple M5 Pro (36GB) is a laptop-tier GPU that can run 45 models natively. It handles 70B-class models at Q4 quantization.
The Apple M5 Pro (36GB) balances capability and value for local LLM inference on MacBook Pro, with 36GB of unified memory and 307 GB/s bandwidth. Qwen 3.6 35B fits at Q4_K_M with headroom, and Gemma 4 31B runs at Q4_K_M or Q8_0 entirely in-memory — delivering the two most-searched open-weight models locally via MLX and llama.cpp without the premium of higher-memory tiers. This is the sweet spot for Apple Silicon users who want serious 30B-class inference in a thin-and-light Mac.
Apple M5 Pro (36GB): 2026 mid-tier M5 Pro — 36GB unified LPDDR5X at 307 GB/s, 15-core CPU (5P+10E).
Qwen 3.6 35B fits at Q4_K_M with headroom; Gemma 4 31B runs at Q4_K_M or Q8_0 entirely in-memory. ~9-14 t/s for 7B via MLX.
MLX and llama.cpp Metal fully supported. The sweet spot for 30B-class local inference in a thin-and-light MacBook Pro.
| Vendor | Apple |
| Architecture | Apple M5 Pro |
| CPU cores | 15 (5S + 10P) |
| VRAM | 36 GB (unified) |
| Memory type | LPDDR5X |
| Memory bandwidth | 307 GB/s |
| Compute backend | METAL |
| Tier | Laptop |
| Released | 2026 |
| Models (native) | 45 / 81 |
| Models (offload) | 0 / 81 |
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 (45)
- Mixtral 8x7B Instruct v0.146.7B · MMLU-Pro 29.7Q3_K_M · ~11.3 t/s
- Command-R 35B35B · MMLU-Pro 33.0Q2_K · ~10.2 t/s
- Qwen 3.5 35B-A3B (MoE)35B · MMLU-Pro 85.3Q4_K_M · ~39.2 t/s
- Qwen 3.6 35B35B · MMLU-Pro 85.2Q4_K_M · ~10.5 t/s
- Yi 1.5 34B Chat34.4B · MMLU-Pro 37.0Q4_K_M · ~10.7 t/s
Show 40 more
- Qwen3 32B32.8B · MMLU-Pro 65.5Q5_K_M · ~9.9 t/s
- Qwen 2.5 32B Instruct32.5B · MMLU-Pro 69.0Q4_K_M · ~11.2 t/s
- Qwen 2.5 Coder 32B Instruct32.5B · MMLU-Pro 50.4Q4_K_M · ~11.2 t/s
- DeepSeek R1 Distill Qwen 32B32.5B · MMLU-Pro 65.0Q4_K_M · ~11.2 t/s
- Nemotron 3 Nano 30B32B · MMLU-Pro 78.3Q5_K_M · ~32.5 t/s
- Gemma 4 31B31B · MMLU-Pro 85.2Q4_K_M · ~11.1 t/s
- Qwen3 30B-A3B (MoE)30B · MMLU-Pro 61.5Q5_K_M · ~31 t/s
- Gemma 2 27B Instruct27.2B · MMLU-Pro 38.0Q5_K_M · ~10.9 t/s
- Gemma 3 27B Instruct27B · MMLU-Pro 67.5Q6_K · ~10.4 t/s
- Qwen 3.6 27B27B · MMLU-Pro 86.2Q6_K · ~10.8 t/s
- Bonsai 27B27B · MMLU-Pro 81.5Ternary (Q2_0) · ~27.9 t/s
- Gemma 4 26B (MoE)26B · MMLU-Pro 82.6Q6_K · ~22.8 t/s
- Mistral Small 3.1 24B Instruct24B · MMLU-Pro 66.8Q6_K · ~11.7 t/s
- Mistral Small 22B22.2B · MMLU-Pro 49.2Q6_K · ~12.2 t/s
- GPT-OSS 20B21B · MMLU-Pro 67.9Q8_0 · ~16.8 t/s
- Qwen3 14B14.8B · MMLU-Pro 61.0Q8_0 · ~14.4 t/s
- Qwen 2.5 14B Instruct14.7B · MMLU-Pro 63.7Q8_0 · ~14.2 t/s
- Phi-4 14B Instruct14B · MMLU-Pro 70.4Q8_0 · ~15.1 t/s
- Mistral Nemo 12B Instruct12.2B · MMLU-Pro 35.6Q8_0 · ~17.2 t/s
- Gemma 3 12B Instruct12.2B · MMLU-Pro 60.6Q8_0 · ~17.5 t/s
- Gemma 4 12B (Unified)12B · MMLU-Pro 77.2Q8_0 · ~15.4 t/s
- Gemma 2 9B Instruct9.2B · MMLU-Pro 32.0BF16 · ~11.6 t/s
- Llama 3.1 8B Instruct8B · MMLU-Pro 48.3BF16 · ~14.4 t/s
- DeepSeek R1 Distill Llama 8B8B · MMLU-Pro 41.0BF16 · ~14.4 t/s
- Qwen3 8B8B · MMLU-Pro 56.7BF16 · ~14.3 t/s
- Qwen 2.5 7B Instruct7.6B · MMLU-Pro 56.3BF16 · ~15.7 t/s
- Mistral 7B Instruct v0.37.25B · MMLU-Pro 30.0BF16 · ~15.8 t/s
- Gemma 3 4B Instruct4B · MMLU-Pro 43.6FP32 · ~14.9 t/s
- Gemma 4 E4B4B · MMLU-Pro 69.4FP32 · ~14.4 t/s
- Phi-3.5 Mini Instruct3.8B · MMLU-Pro 47.4FP32 · ~13.3 t/s
- Llama 3.2 3B Instruct3.2B · MMLU-Pro 24.0FP32 · ~17.9 t/s
- Qwen 2.5 3B Instruct3.1B · MMLU-Pro 32.4FP32 · ~19.3 t/s
- Gemma 2 2B Instruct2.6B · MMLU-Pro 17.8FP32 · ~21.8 t/s
- Gemma 4 E2B2B · MMLU-Pro 60.0FP32 · ~29.2 t/s
- SmolLM2 1.7B Instruct1.7B · MMLU-Pro 19.0FP32 · ~29.2 t/s
- Qwen 2.5 1.5B Instruct1.5B · MMLU-Pro 16.8FP32 · ~39.4 t/s
- Llama 3.2 1B Instruct1.24B · MMLU-Pro 12.5FP32 · ~47 t/s
- Gemma 3 1B Instruct1B · MMLU-Pro 14.7FP32 · ~56.8 t/s
- Qwen 2.5 0.5B Instruct0.5B · MMLU-Pro 10.0FP32 · ~116.9 t/s
- SmolLM2 360M Instruct0.36B · MMLU-Pro 8.0FP32 · ~138.3 t/s
Too large for this GPU (36)
- Llama 3.3 70B Instruct
- Qwen 2.5 72B Instruct
- DeepSeek R1 Distill Llama 70B
- Llama 3.1 70B Instruct
- Mixtral 8x22B Instruct v0.1
- Llama 3.1 405B Instruct
- DeepSeek V3 671B
- DeepSeek R1 671B
- Llama 4 Scout 109B
- Llama 4 Maverick 400B
- Qwen3 235B-A22B (MoE)
- MiniMax M1 456B
- GPT-OSS 120B
- GLM-4.5 355B
- GLM-4.5 Air 106B
- GLM-4.6 355B
- GLM-4.6V 106B
- 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
- GLM-5.2 753B
- Nemotron 3 Ultra 550B-A55B
- Step 3.5 Flash
- Step 3.7 Flash
- MiMo V2.5 Pro
- Kimi K2.5
- MiniMax M3
- Inkling
- Kimi K3
Frequently asked questions
- How much VRAM does the Apple M5 Pro (36GB) have?
- The Apple M5 Pro (36GB) has 36 GB of LPDDR5X with 307 GB/s memory bandwidth (unified system memory, shared between CPU and GPU).
- What is the Apple M5 Pro (36GB) best for?
- With 36 GB of VRAM, the Apple M5 Pro (36GB) is well-suited for running 7B–32B models at Q4 with room for context, making it a great all-rounder for local LLM inference.
- What LLMs can the Apple M5 Pro (36GB) run locally?
- The Apple M5 Pro (36GB) can run 45 of the 81 open-weight models tracked by CanItRun natively in VRAM at 8k context. Top options include: Llama 3.1 8B Instruct at BF16, Llama 3.2 3B Instruct at FP32, Llama 3.2 1B Instruct at FP32.
- Can the Apple M5 Pro (36GB) run Llama 3.3 70B Instruct?
- The Apple M5 Pro (36GB) does not have enough VRAM to run Llama 3.3 70B Instruct. You would need more VRAM or a lower quantization level.
- Can the Apple M5 Pro (36GB) run Qwen 3.6 27B?
- Yes. The Apple M5 Pro (36GB) runs Qwen 3.6 27B natively in VRAM at Q6_K quantization, achieving approximately 10.8 tokens per second.
- Can the Apple M5 Pro (36GB) run Llama 3.1 8B Instruct?
- Yes. The Apple M5 Pro (36GB) runs Llama 3.1 8B Instruct natively in VRAM at BF16 quantization, achieving approximately 14.4 tokens per second.