Optimizing Large Language Models (LLMs) on CPUs: Techniques for Enhanced Inference and Efficiency
Large Language Models (LLMs) based on the Transformer architecture have made significant technological advancements, particularly in understanding and generating human-like writing for various AI applications.
However, implementing these models in low-resource contexts presents challenges, especially when access to GPU hardware resources is limited. In such cases, CPU-based alternatives become crucial for cost-effective and efficient solutions.
Practical Solutions and Value:
A recent research has introduced an approach to enhance the inference performance of LLMs on CPUs by reducing the KV cache size without compromising accuracy. This optimization is essential for ensuring LLMs operate effectively with limited resources.
Additionally, a technique for distributed inference optimization using the oneAPI Collective Communications Library has been proposed. This method significantly improves the scalability and performance of LLMs by enabling effective communication and processing among multiple CPUs.
The team has also provided unique LLM optimization methods on CPUs, such as SlimAttention, compatible with popular models and featuring distinct optimizations for LLM procedures and layers.
By implementing these optimizations, the goal is to accelerate LLMs on CPUs, making them more affordable and accessible for deployment in low-resource settings.
For more details, you can check out the Paper and GitHub.
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