
AstonJ
DeepSeek (671B) running on a cluster of 8 Mac Mini Pros with 64GB RAM each
This is cool!
DEEPSEEK-V3 ON M4 MAC: BLAZING FAST INFERENCE ON APPLE SILICON
We just witnessed something incredible: the largest open-source language model flexing its muscles on Apple Silicon. We’re talking about the massive DeepSeek-V3 on M4 Mac, specifically the 671 billion parameter model running on a cluster of 8 M4 Pro Mac Minis with 64GB of RAM each – that’s a whopping 512GB of combined memory!
This isn’t just about bragging rights. It opens up new possibilities for researchers, developers, and anyone interested in pushing the boundaries of AI. Let’s dive into the details and see why DeepSeek-V3 on M4 Mac is such a big deal.
TABLE OF CONTENTS
- The Results Are In: DeepSeek V3 671B Performance on the M4 Mac Mini Cluster
- Why So Fast? Understanding the DeepSeek-V3 on M4 Mac Performance Advantage
- Exploring Key Considerations: Power, Cost, and Alternative Setups for Running DeepSeek-V3
- Conclusion: The Future of LLM Inference on Apple Silicon with DeepSeek-V3 on M4 Mac
First Post!

AstonJ
We just got the biggest open-source model running on Apple Silicon.
Without further ado, here are the results running DeepSeek v3 (671B) on a 8 x M4 Pro 64GB Mac Mini Cluster (512GB total memory):
Model Time-To-First-Token (TTFT) in seconds Tokens-Per-Second (TPS) DeepSeek V3 671B (4-bit) 2.91 5.37 Llama 3.1 405B (4-bit) 29.71 0.88 Llama 3.3 70B (4-bit) 3.14 3.89 Wait, Deepseek has 671B parameters and runs faster than Llama 70B?
Yes!
Let me explain…
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