Apple M5 Ultra — Local LLM Performance & Compatibility
作者： Jakub Rusinowski · 最后更新： 2026年9月19日
Apple的四芯片Ultra：最高36个CPU核心、80个GPU核心和512 GB统一内存，带宽约1.2 TB/s——这是普通人能直接买到的机器中内存带宽最高的一台，容量足以装下原本需要租用服务器才能运行的模型。
Technical Specifications
| VRAM | 512 GB unified memory |
| Memory Bandwidth | 1229 GB/s |
| TDP | 120 W |
| Architecture | ARM, 3nm TSMC |
| Release Year | 2026 |
| MSRP at Launch | $0 |
| Inference Speed (Llama 3.1 8B Q4_K_M) | 59–123 tok/s (estimated) |
| Inference Speed (Llama 3.3 70B Q4_K_M) | 9.6–20 tok/s (estimated) |
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LLMs Compatible with 512 GB Unified Memory
All models below run comfortably in 512 GB unified memory with Q4_K_M quantization.
| DeepSeek V3 | DeepSeek V3 (685B MoE) · 414 GB VRAM · Q4_K_M · ollama run deepseek-v3 |
| DeepSeek R1 | DeepSeek R1 (671B) · 406 GB VRAM · Q4_K_M · ollama run deepseek-r1:671b |
| Qwen3-Coder | Qwen3-Coder 480B-A35B (MoE) · 291 GB VRAM · Q4_K_M · ollama run qwen3-coder:480b-a35b |
| MiniMax M3 | MiniMax M3 428B-A23B · 259 GB VRAM · Q4_K_M · minimax-m3 |
| Llama 4 | Llama 4 Maverick 17B · 242 GB VRAM · Q4_K_M · ollama run llama4:maverick |
| Qwen 3.5 | Qwen 3.5 397B-A17B · 240 GB VRAM · Q4_K_M · qwen3-5 |
| Nex-N2 | Nex-N2 Pro · 240 GB VRAM · Q4_K_M · nex-n2 |
| GLM-5.3-Flash | GLM-5.3-Flash 320B-A18B · 194 GB VRAM · Q4_K_M · glm-5-3-flash |
75 more families also fit 512 GB — browse the full model library.
Best Use Cases
- 512 GB unified memory
- frontier models locally
- Mac Studio
FAQ
Can the Apple M5 Ultra run local LLMs?
Yes — the Apple M5 Ultra has 512 GB unified memory and runs Apple的四芯片Ultra：最高36个CPU核心、80个GPU核心和512 GB统一内存，带宽约1.2 TB/s——这是普通人能直接买到的机器中内存带宽最高的一台，容量足以装下原本需要租用服务器才能运行的模型。
How fast is the Apple M5 Ultra for AI inference?
The Apple M5 Ultra is estimated to run Llama 3.1 8B at 59–123 tok/s with Q4_K_M quantization. For Llama 3.3 70B the estimate is 9.6–20 tok/s. These are modelled estimates, not measurements — see /en/methodology.
What LLMs can I run on 512 GB VRAM?
With 512 GB you can run: DeepSeek V3, DeepSeek R1, Qwen3-Coder, MiniMax M3, Llama 4. Use Ollama for the easiest setup: ollama run llama3.1:8b.
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