The content discusses the challenges of running large language models (LLMs) on consumer hardware, particularly when the model size exceeds the available GPU memory. To address this, quantization is often applied to reduce the model size. However, even after quantization, the model may still be too large to fit on the GPU.
An alternative approach is to run the model on the CPU RAM using a framework optimized for CPU inference, such as llama.cpp. Intel has developed a new framework called Neural Speed, which is built on top of Hugging Face Transformers and aims to further accelerate inference for 4-bit LLMs on CPUs.
The key optimizations in Neural Speed include:
According to Intel, using Neural Speed can make inference up to 40x faster than llama.cpp for 4-bit LLMs on consumer CPUs.
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by Benjamin Mar... 場所 towardsdatascience.com 04-18-2024
https://towardsdatascience.com/neural-speed-fast-inference-on-cpu-for-4-bit-large-language-models-0d611978f399深掘り質問