The video reviews the performance of the DeepSeek R1 large language model on various Apple Silicon MacBooks, highlighting the significant differences in speed and efficiency across models like the M1, M2, M3, and M4 Max. It emphasizes the importance of hardware specifications for running LLMs locally and provides guidance on using tools like Olama and LM Studio for optimal performance while ensuring user privacy by avoiding external servers.
The video discusses the capabilities of the DeepSeek R1 large language model and its performance on various Apple Silicon MacBooks, including the M1, M2, M3, and M4 Max models. The presenter emphasizes the importance of hardware when running large language models (LLMs) locally, as the performance can vary significantly based on the machine’s specifications. While DeepSeek R1 is open-source and can be run on various devices, including Raspberry Pi and Jetson Nano, the focus of the video is on testing it across different MacBook models to showcase the differences in speed and quality of results.
To run DeepSeek R1, the presenter introduces several tools, including Olama and LM Studio, which simplify the installation and execution of the model. The video provides a step-by-step guide on how to download and set up these tools on macOS. The presenter highlights the ease of use of these applications, allowing users to run models with minimal technical knowledge. The video also explains the concept of model quantization, which reduces the model size for compatibility with lower-spec hardware while potentially sacrificing some quality.
The presenter runs the DeepSeek R1 model on each MacBook, starting with the 1.5 billion parameter version. The performance metrics, such as tokens per second, are recorded for each machine. The M4 Max shows the highest performance, processing text at a rate of 162 tokens per second, while the M1 and M2 models demonstrate slower speeds. The video illustrates how larger models require more RAM and processing power, with the M4 Max being able to handle larger models more efficiently than the others.
In addition to Olama, the video explores LM Studio, which offers more versatility and optimization for Apple Silicon. The presenter compares the performance of different quantized versions of the models, noting that the MLX format provides better optimization and speed on Apple hardware. The video showcases the differences in performance between the GGUF and MLX models, with MLX generally yielding better results, especially for larger models.
Finally, the presenter warns against using the official DeepSeek website to run the model, as it may compromise user data by sending it to external servers. Instead, the video encourages viewers to run the model locally for better privacy and control. The video concludes with a reminder of the importance of hardware in determining the performance of LLMs and invites viewers to explore further content on the channel for more insights into running AI models effectively.