How Is LLaMa.cpp Possible?

In this web content, the author discusses the possibility of running large language models (LLMs) locally on various hardware devices by rewriting the LLaMa inference code in raw C++ and optimizing it. The author highlights the surprising fact that it is possible to run a 7B parameter model locally on a Pixel5 smartphone at 1 tokens/s, a M2 Macbook Pro at ~16 tokens/s, and even a 4GB RAM Raspberry Pi at 0.1 tokens/s. The author dives into the math behind the inference requirements, emphasizing that memory bandwidth is the bottleneck for inference. The use of quantization, which reduces memory requirements, is highlighted as a clever technique. Overall, the author emphasizes the importance of reducing memory requirements and highlights the potential of distillation and training smaller models for longer.

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