DeepSeek inference on M4 MacBook Air

Overview of DeepSeek Models

- DeepSeek-V3: This model is a large Mixture-of-Experts (MoE) model with 671 billion parameters. It has been successfully run on a cluster of M4 Mac Minis, showcasing the efficiency of Apple Silicon for large language models[1].
- DeepSeek R1: Released in January 2025, this model is open-source and rivals GPT models. It offers various quantized versions to reduce size and hardware requirements, making it more feasible for local deployment on devices like the M4 MacBook Air[2].

Running DeepSeek on M4 MacBook Air

Key Considerations

1. Memory and Model Size: The M4 MacBook Air typically comes with less RAM compared to the M4 Mac Mini cluster used for DeepSeek-V3. You'll need to select a smaller or quantized version of the DeepSeek model to fit within the available RAM[2].

2. Unified Memory Architecture: Apple Silicon's unified memory architecture is beneficial for running large models efficiently, especially when generating responses one at a time[1].

3. Quantization and Model Selection: For running on an M4 MacBook Air, you might consider quantized versions of DeepSeek R1, such as the 14B model, which can run with some RAM to spare[2].

Steps to Run DeepSeek Locally

1. Install Ollama: Use Ollama to manage local LLMs on your MacBook Air. It allows you to install and run various models, including DeepSeek[2].

2. Choose a Model: Select a quantized version of DeepSeek R1 that fits within your MacBook Air's RAM. Models like DeepSeek-R1-Distill-Qwen-14B are suitable options[2].

3. Run the Model: Use commands like `exo run deepseek-r1 --devices M4-Pro,M4-Max --quantization 4-bit` to run the model with quantization for better performance on M-series chips[4].

Conclusion

Running DeepSeek inference on an M4 MacBook Air is feasible with the right model selection and quantization. The unified memory architecture of Apple Silicon provides a performance advantage, especially for single-response generation scenarios. However, the limited RAM compared to server setups means you'll need to opt for smaller or quantized models to ensure smooth operation.